1 00:00:00,040 --> 00:00:05,480 Q12025 FDA rolls increase from around 800% to around 1000%. 2 00:00:05,480 --> 00:00:09,440 There 6 to 70% of an AI project now depends on adoption, not 3 00:00:09,440 --> 00:00:12,480 just engineering and and coding, but being able to. 4 00:00:12,480 --> 00:00:15,760 Co innovate with customers, Adaptability is very important, 5 00:00:15,760 --> 00:00:17,920 leadership becomes very important and the soft skills as 6 00:00:17,920 --> 00:00:19,800 well. Being able to rapidly iterate 7 00:00:19,840 --> 00:00:23,280 and deploy it is a career that is definitely worth to go ahead. 8 00:00:23,280 --> 00:00:26,080 With. Forward Deployed Engineer, one 9 00:00:26,080 --> 00:00:30,280 of the hottest jobs in AI right now, with hiring up 800% this 10 00:00:30,280 --> 00:00:32,759 year alone, and it doesn't look like it's slowing down. 11 00:00:33,120 --> 00:00:36,440 We discussed exactly why that is, what the skills are that are 12 00:00:36,440 --> 00:00:39,560 needed, and why you should care if you're a software engineer 13 00:00:39,560 --> 00:00:43,680 right now more than ever. Joining me today is MOFA Gear, 14 00:00:43,960 --> 00:00:47,680 Principal Technical Consultant over at ServiceNow, specifically 15 00:00:47,680 --> 00:00:51,680 in AI, and he's built and shaped many forward deployed 16 00:00:51,680 --> 00:00:53,680 engineering teams. So enjoy. 17 00:00:57,240 --> 00:00:59,080 Are you currently a forward deployed engineer? 18 00:00:59,080 --> 00:01:01,000 Because I I looked at your resume, I looked at LinkedIn, 19 00:01:01,000 --> 00:01:03,680 but it it doesn't state explicitly indeed forward 20 00:01:03,680 --> 00:01:06,880 deployed engineer. Yes, I'm not myself a forward 21 00:01:06,880 --> 00:01:10,640 deployed engineer, but I shaped and and helped shape the teams 22 00:01:10,640 --> 00:01:13,440 of forward deployed engineers, whether it's at service now and 23 00:01:13,440 --> 00:01:16,600 at many other customers there as well here. 24 00:01:17,040 --> 00:01:21,040 The reason being again, like in terms of the birth of, of the 25 00:01:21,040 --> 00:01:24,520 role and, and how it started. If you look at the trends in, in 26 00:01:24,520 --> 00:01:28,720 the market there, the market used to go ahead and, and value 27 00:01:28,720 --> 00:01:32,960 four years of, of the skills and, and universities that used 28 00:01:32,960 --> 00:01:36,680 to be the, the economic marketing or power signal There 29 00:01:36,680 --> 00:01:42,000 itself, 22% of the premium was set on, on a master's degree. 30 00:01:42,480 --> 00:01:44,240 But the market now is shifting away from that. 31 00:01:44,880 --> 00:01:49,080 And we already see that in terms of AI being at a much higher 32 00:01:49,080 --> 00:01:51,440 premium at, in, in current rates. 33 00:01:51,440 --> 00:01:54,720 Not only that, if you look at it overall in terms of software 34 00:01:54,720 --> 00:02:00,840 engineering, Q12023 to Q12025, it already dropped by around 35 00:02:00,840 --> 00:02:02,840 70%. You'll get it on. 36 00:02:02,840 --> 00:02:05,480 On the other side, if you just take from last year to this year 37 00:02:05,840 --> 00:02:10,520 as FDA rolls increase from around 800% to around 1000% 38 00:02:10,520 --> 00:02:14,360 there. So that boom and that rise in AI 39 00:02:14,800 --> 00:02:18,480 mainly been already recognised adoption is one of the the main 40 00:02:18,480 --> 00:02:19,920 bottlenecks there. It's not about the the 41 00:02:19,920 --> 00:02:23,480 capability there anymore. So helping customers rewire that 42 00:02:23,480 --> 00:02:28,040 entire their entire workflows and all their their systems to 43 00:02:28,040 --> 00:02:30,200 this AI native future is absolutely paramount. 44 00:02:30,240 --> 00:02:34,560 And as engineers, that is that evolution to that FDS is 45 00:02:34,560 --> 00:02:35,960 absolutely paramount there as well. 46 00:02:36,120 --> 00:02:39,840 With all the new skills that are are coming about understanding 47 00:02:39,840 --> 00:02:42,840 the the end to end cycle of taking data-driven frameworks, 48 00:02:43,080 --> 00:02:46,280 starting the integration, being able to do easily take into 49 00:02:46,280 --> 00:02:50,200 account all the different systems, put that or bring about 50 00:02:50,200 --> 00:02:53,520 one data there in order to be able to to get the value of AI 51 00:02:53,520 --> 00:02:55,200 and expand it across the organization. 52 00:02:55,480 --> 00:02:58,520 Why can't customers do the implementation when it comes to 53 00:02:58,520 --> 00:03:01,600 AI or tooling themselves in house necessarily? 54 00:03:01,880 --> 00:03:07,160 Now, in terms of AI as, as we know it has boomed significantly 55 00:03:07,440 --> 00:03:12,160 and only that the skills, if you look at the talent war itself 56 00:03:12,160 --> 00:03:15,440 in, in terms of AI between all the big organizations there 57 00:03:15,440 --> 00:03:18,840 itself, there are limited people who understand AI very deeply 58 00:03:18,840 --> 00:03:23,840 there itself only that number 2, not only do you need the the the 59 00:03:23,840 --> 00:03:26,720 skill sets that a person who understands AI very well there 60 00:03:26,720 --> 00:03:28,480 as well. We need someone who understands 61 00:03:28,560 --> 00:03:32,040 all the system architecture as well as all the innings in terms 62 00:03:32,040 --> 00:03:34,600 of of the company there. And when I bring those two 63 00:03:34,600 --> 00:03:38,520 together, that's what leads to to success part that is is 64 00:03:38,520 --> 00:03:40,520 missing. There is essentially the skills 65 00:03:40,840 --> 00:03:45,040 and the talent to be able first to deploy of the the talent 66 00:03:45,040 --> 00:03:48,800 there is actually with the the frontier labs or with the the 67 00:03:48,800 --> 00:03:51,320 big organizations there. So that's where it's very 68 00:03:51,320 --> 00:03:54,400 important to help those organizations to be able to to 69 00:03:54,600 --> 00:03:57,720 take that next step there. In terms of adoption from just 70 00:03:57,720 --> 00:04:00,360 the capability side, where forward deployed engineers are 71 00:04:00,360 --> 00:04:01,760 are becoming absolutely paramount. 72 00:04:02,000 --> 00:04:05,640 We've seen this start with volunteer from there go across 73 00:04:05,640 --> 00:04:08,480 the the ecosystem, as you've noted, now one of the hottest 74 00:04:08,480 --> 00:04:13,160 jobs in in the market as in terms of of the role there 75 00:04:13,160 --> 00:04:17,360 itself and like customers, especially now, yes, I have a 76 00:04:17,360 --> 00:04:19,880 capability and AII can take it off the shelf. 77 00:04:20,279 --> 00:04:23,160 But these off the shelf solutions, many a times you 78 00:04:23,160 --> 00:04:27,160 realise they they still need to I still need to do some aspect 79 00:04:27,160 --> 00:04:29,880 or need to tune it in order to be able to, to get to success. 80 00:04:30,440 --> 00:04:34,320 And that at whether it's at at service now or or majority of 81 00:04:34,320 --> 00:04:38,240 our customers, we are we do have the market leading products and 82 00:04:38,240 --> 00:04:41,040 you can take it and already in terms of of deployment there we 83 00:04:41,040 --> 00:04:43,760 have reduced it significantly. But what we have we have 84 00:04:43,760 --> 00:04:46,760 realised is if we do have forward deployed engineers 85 00:04:46,760 --> 00:04:49,840 sitting with customers Co innovating, understanding they 86 00:04:49,840 --> 00:04:51,720 already understand the product very well. 87 00:04:52,000 --> 00:04:54,720 But then I need the customer side of it and being able to go 88 00:04:54,720 --> 00:04:56,920 ahead and understand their entire architecture when I have 89 00:04:56,920 --> 00:05:00,080 someone sitting there with them Co innovating, that's what takes 90 00:05:00,080 --> 00:05:05,120 them to to that success there. So the ROI in terms of AI 91 00:05:05,120 --> 00:05:08,840 projects there is significantly taken is much higher in terms of 92 00:05:08,840 --> 00:05:12,320 success when versus I compare it to to other projects when I have 93 00:05:12,320 --> 00:05:16,120 F DS also involved there. That skill set like I, I noted 94 00:05:16,520 --> 00:05:21,840 is currently limited in in DD market with this innovation and 95 00:05:21,840 --> 00:05:25,440 with this evolution to of the the, the role there, that is 96 00:05:25,440 --> 00:05:27,800 what is going to to take the market to to the next level 97 00:05:27,800 --> 00:05:30,840 there as well. Yeah, While looking into for 98 00:05:30,840 --> 00:05:33,720 deployed engineers, I currently work at a consultancy. 99 00:05:33,720 --> 00:05:36,840 So I already go to clients and I do custom software development, 100 00:05:36,840 --> 00:05:39,680 high code, very much understanding customer base, 101 00:05:39,680 --> 00:05:43,200 business constraints, technical constraints, existing landscape 102 00:05:43,200 --> 00:05:46,080 or going from zero to 1. Those are kind of the the 103 00:05:46,080 --> 00:05:47,560 various scenes that I've been in. 104 00:05:47,560 --> 00:05:49,800 So I already have to be adaptable. 105 00:05:50,320 --> 00:05:52,840 And then I read what 4 deployed engineers are doing. 106 00:05:53,560 --> 00:05:56,280 It's very similar to what I'm doing right now. 107 00:05:56,280 --> 00:05:58,920 So I'm strongly considering going and taking that step. 108 00:05:59,440 --> 00:06:01,480 But for people that are listening, mainly software 109 00:06:01,480 --> 00:06:04,520 engineers or people in tech, what would you advise them if 110 00:06:04,520 --> 00:06:08,400 they are considering this? Is it a feasible career path in 111 00:06:08,400 --> 00:06:11,200 general? A. 100%, very, very good 112 00:06:11,520 --> 00:06:14,000 question. And yes, so when I look at the, 113 00:06:14,200 --> 00:06:18,200 the software engineering 1st role itself, we've already 114 00:06:18,200 --> 00:06:21,280 started seeing an evolution of that software engineering role 115 00:06:21,280 --> 00:06:24,640 itself. Previously all the, the mundane, 116 00:06:25,000 --> 00:06:28,320 those tasks in terms of just coding it or, or being able to 117 00:06:28,320 --> 00:06:31,360 code, right, unit tests, all of those are augmented away now by 118 00:06:31,360 --> 00:06:33,800 AI. We've already seen that in, in 119 00:06:33,800 --> 00:06:35,920 the market as well. The market values, like I 120 00:06:35,920 --> 00:06:40,640 already noted, AI skills much more than than just now I'm 121 00:06:40,640 --> 00:06:44,480 looking at, rather than looking at the, the experience of the 122 00:06:44,680 --> 00:06:47,040 person, I'm actually looking at the skills that they, they have. 123 00:06:47,040 --> 00:06:49,640 And that's what like I noted, the, the market is evaluating 124 00:06:49,640 --> 00:06:50,960 much more. So we take it from that 125 00:06:51,600 --> 00:06:56,440 perspective, evolution of the engineers rather than just like 126 00:06:56,480 --> 00:06:59,720 doing those, those mundane tasks, being able to do the 127 00:06:59,720 --> 00:07:03,880 strategic validation, being able to to go ahead and look at an 128 00:07:03,880 --> 00:07:08,040 end to end system integration, adaptable, not just engineering 129 00:07:08,040 --> 00:07:11,680 and and coding, but being able, like I know to Co innovate with 130 00:07:11,680 --> 00:07:13,560 customers. So adaptability is very 131 00:07:13,560 --> 00:07:15,800 important, leadership becomes very important and the soft 132 00:07:15,800 --> 00:07:19,400 skills as well. So yes, in terms of the skill 133 00:07:19,400 --> 00:07:23,480 set on the technical side of it, we evolved there from just the, 134 00:07:23,640 --> 00:07:27,200 the typical tasks that we, we used to do there into a higher 135 00:07:27,200 --> 00:07:30,320 level where I can validate, I can do QEI can go ahead 136 00:07:30,320 --> 00:07:33,680 integration, which was the, the failure of most software 137 00:07:33,680 --> 00:07:35,920 projects as well and and getting the ROI with them. 138 00:07:36,160 --> 00:07:37,800 That again is is absolutely paramount. 139 00:07:37,880 --> 00:07:40,240 But again, also on the the soft skills as well. 140 00:07:40,240 --> 00:07:43,440 Are very very. Critical for me to go ahead and 141 00:07:43,440 --> 00:07:46,080 and be able to derive and work with the customer to get the 142 00:07:46,080 --> 00:07:47,600 success and objections they're looking for. 143 00:07:47,920 --> 00:07:50,960 Yeah, when looking into the skills, I just thought, man, 144 00:07:51,040 --> 00:07:54,280 there are a lot of skills that are fitted in this role in this 145 00:07:54,280 --> 00:07:56,440 person. On the technical side, I read 146 00:07:56,840 --> 00:08:01,640 since it is very much driven by AI adoption like Python, 147 00:08:01,960 --> 00:08:05,280 TypeScript is a lot with regards to front end frameworks, which 148 00:08:05,280 --> 00:08:07,080 also means you need to know your front end frameworks. 149 00:08:07,080 --> 00:08:09,400 So front end and back end specifically, you need to be 150 00:08:09,400 --> 00:08:11,360 able to deploy. So cloud native technology, 151 00:08:11,360 --> 00:08:15,600 whether it be AWS, Google Cloud or Azure in this case where you 152 00:08:15,600 --> 00:08:17,600 deploy or how you deploy. So Docker and Kubernetes you 153 00:08:17,600 --> 00:08:19,920 need to be aware of. And then you have everything 154 00:08:19,920 --> 00:08:23,720 with regards to AI. So not just calling APIs because 155 00:08:23,720 --> 00:08:26,600 a lot of people can call an API, but what does it mean to embed a 156 00:08:26,600 --> 00:08:29,200 model or a feature within the product specifically? 157 00:08:29,600 --> 00:08:32,600 Or how do you build up and orchestrate agentic systems like 158 00:08:32,600 --> 00:08:34,679 that? From a technical level, it feels 159 00:08:34,679 --> 00:08:36,000 like a lot of depth that you need. 160 00:08:36,320 --> 00:08:42,360 Need I could initially be a bit scary, but again, in terms of of 161 00:08:43,000 --> 00:08:46,200 taking it in into an approach there with the the skill set and 162 00:08:46,200 --> 00:08:50,080 one already has typically the hard skills that that are 163 00:08:50,080 --> 00:08:52,000 required. But essentially the evolution 164 00:08:52,240 --> 00:08:55,800 towards most of of the folks already know about multi cloud 165 00:08:55,800 --> 00:08:59,320 technologies about the container orchestration. 166 00:08:59,320 --> 00:09:03,280 So that typically people have then the the more important side 167 00:09:03,280 --> 00:09:07,280 of it becomes whether I look at it, it's an end to end scale 168 00:09:07,280 --> 00:09:10,200 there not I'm really looking at it from the scope of just the 169 00:09:10,200 --> 00:09:12,800 engineering or infrastructure side of it, but an end to end 170 00:09:13,160 --> 00:09:15,680 approach. So when I say end to end, I 171 00:09:15,680 --> 00:09:19,760 start what is important there in terms of an AI project, there is 172 00:09:19,760 --> 00:09:22,440 not looking at it that at an entire scale there as well 173 00:09:22,640 --> 00:09:27,360 taking use cases, high value use cases to production is is very 174 00:09:27,360 --> 00:09:29,840 important. So when we take it from, from 175 00:09:29,840 --> 00:09:32,600 that approach as well, understanding the ability of the 176 00:09:32,600 --> 00:09:35,600 system, analysing it and the data science in order to 177 00:09:35,680 --> 00:09:38,760 evaluate, find those, those high value use cases and then 178 00:09:38,760 --> 00:09:42,840 accordingly be able to, to translate that from, from a POC 179 00:09:42,880 --> 00:09:45,360 all the way to from zero to 1 essentially. 180 00:09:45,640 --> 00:09:49,760 So when we take all those, those skills together, like you noted, 181 00:09:49,760 --> 00:09:53,360 whether it's the Python, TypeScript, the front end side 182 00:09:53,360 --> 00:09:56,320 of it as well, and likewise in terms of the back end, but being 183 00:09:56,320 --> 00:09:59,000 able to rapidly iterate and deploy is something very 184 00:09:59,000 --> 00:10:01,960 important. So when we take it as as an 185 00:10:01,960 --> 00:10:05,480 approach there learning and being on the job on doing is 186 00:10:05,480 --> 00:10:09,160 very important rather than just taking like the previous 187 00:10:09,160 --> 00:10:14,000 approach of like noted, A4 year university degree as, as that is 188 00:10:14,000 --> 00:10:16,200 completely changed in terms of what the market values. 189 00:10:16,560 --> 00:10:20,480 So when I, I take back end, front end, those are absolutely 190 00:10:20,840 --> 00:10:24,640 paramount there as well, like integrating into to AP is that 191 00:10:24,640 --> 00:10:28,160 again, is, is one part of it, but it's not just about 192 00:10:28,240 --> 00:10:32,600 engineering, consulting or in, in terms of such a role. 193 00:10:32,960 --> 00:10:36,040 It's about architecting an entire transformative ecosystem 194 00:10:36,040 --> 00:10:39,120 with that customer. So when I take it from from that 195 00:10:39,120 --> 00:10:42,240 approach, starting with those high value potential use cases 196 00:10:42,240 --> 00:10:45,280 that can get a customer to success and then accordingly 197 00:10:45,280 --> 00:10:48,240 being able to, to iterate and then scale it out from there. 198 00:10:48,560 --> 00:10:52,520 So learning how to pinpoint find those high value use cases that 199 00:10:52,520 --> 00:10:56,080 a customer can that can deploy or can get a customer to value. 200 00:10:56,360 --> 00:11:01,680 And then again, we can also as an early career or even even 201 00:11:01,680 --> 00:11:04,360 well had been advanced in in a software engineering role, 202 00:11:04,360 --> 00:11:09,520 looking to to trance or move ahead to, to the FTE path as 203 00:11:09,520 --> 00:11:12,320 well. Here again, taking being able to 204 00:11:12,320 --> 00:11:14,800 take the data science part, understanding it, process 205 00:11:14,800 --> 00:11:17,760 mining, bringing the organization or what are the 206 00:11:17,760 --> 00:11:19,960 processes right now, the workflows that I have there, 207 00:11:20,280 --> 00:11:23,920 being able to again rewire those all to the the engineering age 208 00:11:24,200 --> 00:11:27,840 or rather to the AIH there. Those are all again, paramount 209 00:11:27,840 --> 00:11:31,120 concepts there to do so I can order or to simplify it and 210 00:11:31,120 --> 00:11:33,920 start with the data science and understanding the use cases. 211 00:11:34,160 --> 00:11:37,480 Yes, again, the hard, the hard skills, the engineering part are 212 00:11:37,480 --> 00:11:40,360 again absolutely paramount here. But then finally being able. 213 00:11:40,360 --> 00:11:42,840 To to rapidly. Iterate and deploy that is is 214 00:11:42,840 --> 00:11:45,320 very important. So you take hard skills, 215 00:11:45,320 --> 00:11:47,920 typically we all as as engineers, we'll all have those. 216 00:11:48,240 --> 00:11:51,360 Then to be able to rapidly iterate to have that sense of 217 00:11:51,360 --> 00:11:54,320 urgency and then not only think about it from my engineering 218 00:11:54,320 --> 00:11:58,440 scope, architecting and that transformative, transformative 219 00:11:58,440 --> 00:12:01,280 ecosystem through product centric thinking to that data 220 00:12:01,280 --> 00:12:03,760 science and data-driven frameworks is what will will 221 00:12:03,760 --> 00:12:04,880 lead the person to the next spot. 222 00:12:04,880 --> 00:12:09,640 There could be scary initially, but definitely if you get once 223 00:12:09,640 --> 00:12:12,920 people get their their hands dirty and it's as a quite a 224 00:12:12,920 --> 00:12:16,800 career part for for a person as I have done in in terms of 225 00:12:16,800 --> 00:12:19,880 service now as well. I think the reason why I feel 226 00:12:19,880 --> 00:12:24,160 like it's daunting is because when I look at FD ES and kind of 227 00:12:24,160 --> 00:12:26,880 the skills that are required, there's a certain level of depth 228 00:12:26,960 --> 00:12:29,160 on a lot of various aspects, right? 229 00:12:29,400 --> 00:12:32,080 Being good in one thing when it comes to T shaped profiles or 230 00:12:32,080 --> 00:12:35,080 being good at 2 specific things when it comes to pie shared 231 00:12:35,080 --> 00:12:37,000 profiles. That is no longer what I feel 232 00:12:37,000 --> 00:12:39,640 like is there in for deployed engineers. 233 00:12:39,960 --> 00:12:42,640 And the daunting part is they are asking that of you. 234 00:12:42,960 --> 00:12:47,960 But in practice do FD ES? Like are they solo operators at 235 00:12:47,960 --> 00:12:49,840 customers or they typically work in teams? 236 00:12:49,920 --> 00:12:53,840 Again, here look, it is in teams typically in terms of FDS. 237 00:12:54,200 --> 00:12:57,960 So for instance, if I take the, the model of majority of, of 238 00:12:57,960 --> 00:13:01,760 enterprises, whether including Palantir, ServiceNow, Open AI 239 00:13:01,760 --> 00:13:05,360 and many others, there's forward deployed solution architects, 240 00:13:05,360 --> 00:13:08,400 there's forward deployed product managers as well as the, the 241 00:13:08,400 --> 00:13:11,120 engineers there as well. So it's a make up of a small 242 00:13:11,120 --> 00:13:14,200 nimble team that actually works together in order to, to get 243 00:13:14,200 --> 00:13:17,520 that customer to, to success. So it's not just a, a solo 244 00:13:17,520 --> 00:13:21,680 operator, but then again, that leadership side of it is, is 245 00:13:21,680 --> 00:13:23,680 absolutely paramount. Although it's a small nimble 246 00:13:23,680 --> 00:13:26,680 team, being able, like I noted, to work with that higher urgency 247 00:13:26,680 --> 00:13:29,240 and get that custom to success is, is absolutely paramount 248 00:13:29,240 --> 00:13:32,560 there as well. Take an AI project and compare 249 00:13:32,560 --> 00:13:37,640 it to to other projects there around 60% of 6 to 70% of an AI 250 00:13:37,640 --> 00:13:39,880 project. Now depends the success of it 251 00:13:39,880 --> 00:13:44,360 depends on adoption, 10 to 15, around 10 to 15% depends on on 252 00:13:44,360 --> 00:13:47,480 governance compliance. So if I take those compare it to 253 00:13:47,480 --> 00:13:50,160 a software engineering project, a typical project is much 254 00:13:50,160 --> 00:13:53,680 different. So hence the the the forward 255 00:13:53,680 --> 00:13:56,320 deployed engineer and the importance of them becomes very, 256 00:13:56,600 --> 00:14:00,120 very much more as I compared to to a previous one. 257 00:14:00,120 --> 00:14:03,360 But they're not solo operators. They work in, in small teams. 258 00:14:03,360 --> 00:14:06,560 Solution architects can bring that expertise in terms of 259 00:14:06,800 --> 00:14:09,560 looking at it from the solution side that are we already have 260 00:14:09,560 --> 00:14:11,760 there. And then from the from the our 261 00:14:11,760 --> 00:14:13,960 customer side. How can I architect an an end to 262 00:14:13,960 --> 00:14:16,560 end system looking at it from the responsibility side, 263 00:14:16,560 --> 00:14:17,880 governance side, compliance side. 264 00:14:18,240 --> 00:14:20,240 And likewise, indeed, there is the engineer as well. 265 00:14:20,600 --> 00:14:23,000 Yes, engineer. Now if I compare it again, like 266 00:14:23,000 --> 00:14:28,240 you noted, could be daunting in the sense of AT shaped is, is is 267 00:14:28,240 --> 00:14:32,320 very important. So still having want the Bretton 268 00:14:32,320 --> 00:14:34,080 and Depton and the engineering side of it. 269 00:14:34,400 --> 00:14:38,280 But Gandhi, the product centric side of it, how I can go from 270 00:14:38,280 --> 00:14:41,800 zero to 1 and, and the design UX and all the elements around that 271 00:14:42,160 --> 00:14:45,280 being able likewise to take that from in terms of the 272 00:14:45,280 --> 00:14:46,800 infrastructure side of it as well. 273 00:14:47,400 --> 00:14:50,520 Understanding the the models, the usage that again is, is 274 00:14:50,520 --> 00:14:53,880 absolutely paramount there with, with the the use cases, the ROI 275 00:14:53,880 --> 00:14:57,280 side of it, then that's where the ROI value alignment is, is 276 00:14:57,280 --> 00:15:00,240 absolutely paramount. Those areas bring them together 277 00:15:00,240 --> 00:15:03,920 the solution architecture engineer as well as EDPMS. 278 00:15:03,920 --> 00:15:07,280 That's what can can get again those those success. 279 00:15:07,560 --> 00:15:11,280 So like noted, it's not completely dependent on on DD 280 00:15:11,280 --> 00:15:16,360 engineer, but the skills, yes, the skills needed are more than 281 00:15:16,360 --> 00:15:19,400 than what was typically needed or or valued previously. 282 00:15:19,400 --> 00:15:23,920 Rather than the the writing of all of that code, way I can 283 00:15:23,920 --> 00:15:27,480 generate all of that code there be able rather to being able to 284 00:15:27,480 --> 00:15:30,960 have that, that validation capabilities be able to to 285 00:15:30,960 --> 00:15:35,040 easily take that identifying security issues with the Edu 286 00:15:35,040 --> 00:15:37,880 code there and then accordingly take that to to production. 287 00:15:37,880 --> 00:15:40,160 It's absolute paramount in terms of skill sets. 288 00:15:41,080 --> 00:15:43,880 This sounds like a lot of fun, I must say, because I'm a person 289 00:15:43,880 --> 00:15:47,000 that likes ownership and impact specifically. 290 00:15:47,440 --> 00:15:50,160 And I feel like as forward deployed engineer, you come in 291 00:15:50,160 --> 00:15:52,480 and the customer probably has a feeling of what problem they 292 00:15:52,480 --> 00:15:54,480 have and you might identify that's the right problem. 293 00:15:54,480 --> 00:15:56,880 Or you solve a different problem, actually an underlying 294 00:15:56,880 --> 00:15:58,440 problem that they didn't even know they had. 295 00:15:58,960 --> 00:16:03,080 And you do that and you come in hopefully then with a team that 296 00:16:03,080 --> 00:16:06,360 you feel more secure in like in the one side I I get super 297 00:16:06,360 --> 00:16:08,320 excited. On the other hand, I always have 298 00:16:08,640 --> 00:16:10,880 that feeling of imposter syndrome because there's a lot 299 00:16:11,280 --> 00:16:14,760 and high stakes here. I feel like when you are an FTE, 300 00:16:15,080 --> 00:16:17,360 when I came into CBS specifically, I was very early 301 00:16:17,360 --> 00:16:20,720 in career and the only reason I felt comfortable was because I 302 00:16:20,720 --> 00:16:23,040 knew I was always going to be in a team. 303 00:16:23,440 --> 00:16:26,760 There was a setting where my part of the company was always 304 00:16:26,760 --> 00:16:31,200 working in team settings. So then not like individual 305 00:16:31,200 --> 00:16:33,560 consultants going to clients, I always had the security of 306 00:16:33,560 --> 00:16:36,760 people around me and especially being early in career, that was 307 00:16:36,760 --> 00:16:39,560 what gave me the confidence to also learn and grow and make 308 00:16:39,560 --> 00:16:42,360 mistakes and continuously actually do this. 309 00:16:42,880 --> 00:16:46,280 I'm wondering in an FDE role already right now on the market, 310 00:16:46,840 --> 00:16:48,360 there's very little junior hiring. 311 00:16:48,360 --> 00:16:50,920 People are still looking at very much media and even more 312 00:16:50,920 --> 00:16:53,440 seniors. But you're saying that for 313 00:16:53,440 --> 00:16:56,480 juniors or people early in career specifically, the FDE 314 00:16:56,480 --> 00:16:58,840 path might actually be a really realistic one as well? 315 00:16:59,320 --> 00:17:03,400 Certainly, certainly so very similar to to your story as well 316 00:17:03,440 --> 00:17:06,560 myself. I, I started that at ServiceNow 317 00:17:06,560 --> 00:17:10,400 three years ago as as just an internal associate consultant 318 00:17:10,400 --> 00:17:12,520 there and in the AI field there as well. 319 00:17:12,800 --> 00:17:18,119 Was able to work on on the job, like you've noted, be able to to 320 00:17:18,319 --> 00:17:23,760 get the support of of many great individuals and and teams at at 321 00:17:23,760 --> 00:17:27,920 ServiceNow to help me grow, work with customers and then innovate 322 00:17:27,920 --> 00:17:31,600 and grow to where I'm today as as an AI lead at, at ServiceNow 323 00:17:31,600 --> 00:17:34,400 there. So with that, the ability of 324 00:17:34,440 --> 00:17:39,480 understanding, so even at an an early in in career phase, having 325 00:17:39,480 --> 00:17:43,720 those those skill sets, the market also values that those 326 00:17:44,320 --> 00:17:46,760 those folks early in career are already AI native. 327 00:17:47,120 --> 00:17:50,600 They have from a very early age of already started working with 328 00:17:50,600 --> 00:17:53,800 those capabilities and that's what even research their shows. 329 00:17:54,120 --> 00:17:58,440 So with that, yes, although the there is early in career, the, 330 00:17:58,560 --> 00:18:03,400 the, the hiring has reduced their that should still not shy 331 00:18:03,400 --> 00:18:06,680 people away from from the the role there because like you 332 00:18:06,680 --> 00:18:08,880 noted, there's a lot of excitement and value that can be 333 00:18:08,880 --> 00:18:14,000 obtained with it AI this the pressure is, is is much higher 334 00:18:14,000 --> 00:18:17,200 there in terms of an AI investment into a project or in 335 00:18:17,200 --> 00:18:21,520 a on a product is higher. So the ROI expected there and 336 00:18:21,520 --> 00:18:24,840 the time to value is, is on different level to a software 337 00:18:24,840 --> 00:18:27,640 project. But again, the, the working in 338 00:18:27,640 --> 00:18:31,960 those, the learning side of it, the challenge that one is, is 339 00:18:31,960 --> 00:18:36,960 put out that it is the pace of, of growth and learning is much 340 00:18:36,960 --> 00:18:41,640 farther and rapid than any other role to, to, to my experience, 341 00:18:41,640 --> 00:18:46,320 or at least in, in my opinion, that shapes or grows the person 342 00:18:46,320 --> 00:18:49,680 very quickly there as well. So that I that's why I highly 343 00:18:49,680 --> 00:18:53,560 recommend DD Field there being on the job, being challenged 344 00:18:53,560 --> 00:18:57,840 every day with that pressure there as well to to deliver 345 00:18:57,840 --> 00:19:01,080 value something that is there, but at the same time brings the 346 00:19:01,080 --> 00:19:03,360 best out of a person there as well. 347 00:19:03,840 --> 00:19:06,960 I'm wondering both for people that are early in career or 348 00:19:06,960 --> 00:19:10,080 media or senior Indian, doesn't matter, how do I get in? 349 00:19:10,200 --> 00:19:12,680 How do I in the end secure a position? 350 00:19:12,680 --> 00:19:15,880 As for deployed engineer, what are the skills that are 351 00:19:15,880 --> 00:19:17,080 measured? Maybe even through the 352 00:19:17,080 --> 00:19:19,960 interviewing process or or when you're forming A-Team, what do 353 00:19:19,960 --> 00:19:21,400 you look for? Sure. 354 00:19:21,560 --> 00:19:25,080 If we, we take we talked about the the hard skills there as 355 00:19:25,080 --> 00:19:27,600 well. So if you look at the back end 356 00:19:27,600 --> 00:19:32,640 or front end, the multi cloud technologies, the AWSGCP, those 357 00:19:32,640 --> 00:19:36,560 are all again taken in or required as as part of the role. 358 00:19:36,840 --> 00:19:39,480 And then you take it into the AI side as well. 359 00:19:39,720 --> 00:19:43,360 I mean, there's so many ways to go ahead. 360 00:19:43,360 --> 00:19:47,680 And today, just if you, if you look at the ability to just take 361 00:19:47,680 --> 00:19:51,360 over an AI project online, I can take an API taking and they're 362 00:19:51,360 --> 00:19:53,880 start building out something that that is quite 363 00:19:53,880 --> 00:19:56,640 transformational there as well. I mean, there's even apps, for 364 00:19:56,640 --> 00:20:00,640 instance, like Replict and I can easily wrap prototype as well 365 00:20:00,640 --> 00:20:03,000 develop and from zero to an entire website. 366 00:20:03,000 --> 00:20:07,120 So all those, those skill sets are again, very important. 367 00:20:07,360 --> 00:20:10,560 Being able to iterate and, and learn and show that you have 368 00:20:10,560 --> 00:20:13,920 those, those experiences and those projects that you have 369 00:20:13,920 --> 00:20:15,920 developed are absolutely important there as well. 370 00:20:16,080 --> 00:20:17,920 So that's on the the hard skills. 371 00:20:18,400 --> 00:20:20,440 But if you look at it from the soft skills, again, that is 372 00:20:20,480 --> 00:20:24,000 very, very important. So typically on the the 373 00:20:24,000 --> 00:20:27,840 engineering side, the soft skills, I mean we're valued more 374 00:20:27,840 --> 00:20:32,240 in terms of, of the, the, the codes as well as the products 375 00:20:32,240 --> 00:20:36,040 there that, that we ship. But the soft skills becomes much 376 00:20:36,040 --> 00:20:38,120 more important in terms of an FTE role as well. 377 00:20:38,120 --> 00:20:42,840 So like I noted in terms of the, the value of of and the ROI of 378 00:20:42,840 --> 00:20:46,560 these products are quite massive for the organization's they're 379 00:20:46,560 --> 00:20:48,720 in transformational value that I can bring about. 380 00:20:49,040 --> 00:20:52,360 So at the same time, the, the soft skills, being able to lead 381 00:20:52,360 --> 00:20:55,800 that to success becomes important being adaptable in 382 00:20:55,800 --> 00:20:59,760 terms of pure, I have an A challenge environment. 383 00:21:00,040 --> 00:21:03,320 I need to I need to develop and take something to, to production 384 00:21:03,720 --> 00:21:06,440 at at speed there as well. It's been adaptable. 385 00:21:06,440 --> 00:21:09,400 Working with the customer and Co innovating with them is, is very 386 00:21:09,400 --> 00:21:14,080 important, but also being a leader, being able to innovate, 387 00:21:14,320 --> 00:21:18,120 bringing that visionary thinking is are all skills that that are 388 00:21:18,120 --> 00:21:20,320 important there as well. Obviously collaboration and 389 00:21:20,480 --> 00:21:23,400 communication, partnering with that that customer to be able 390 00:21:23,400 --> 00:21:27,200 to, to take them to success. Those are are the skill sets 391 00:21:27,200 --> 00:21:32,560 that are important, but to simply be able to take that next 392 00:21:32,560 --> 00:21:36,040 step, I would say first step, just get your hands dirty with 393 00:21:36,440 --> 00:21:38,800 with with AI there. There's so much that is out 394 00:21:38,800 --> 00:21:43,080 there today with AI and it's very early, very early innings 395 00:21:43,080 --> 00:21:45,960 for for many people there. So I've been, if you've not yet 396 00:21:46,280 --> 00:21:49,680 started there, whether you're early in career, whether you're 397 00:21:50,000 --> 00:21:53,200 advanced in, in your career there, there's that opportunity 398 00:21:53,200 --> 00:21:56,960 to just take those, those steps and projects online, do it. 399 00:21:57,240 --> 00:21:59,960 And then from there, when you're able to show it, then that's 400 00:21:59,960 --> 00:22:02,560 what the, the market values now as well. 401 00:22:02,920 --> 00:22:05,280 Like, you know, we are, we're moving to a skills based economy 402 00:22:05,400 --> 00:22:08,720 and we see that already in the biggest in, in the biggest 403 00:22:08,720 --> 00:22:12,880 enterprises and, and also how the, the, the global market is, 404 00:22:12,880 --> 00:22:15,520 is moving. So moving away from just 405 00:22:15,880 --> 00:22:18,440 experience to skills based. So if I'm looking at those 406 00:22:18,440 --> 00:22:22,120 skills based, being able to to learn, prove and show my skills 407 00:22:22,120 --> 00:22:25,840 is much more important than than just having those. 408 00:22:25,840 --> 00:22:29,040 Those whether it's any certifications or anything else, 409 00:22:29,040 --> 00:22:31,440 those are great obviously, and they add a lot very important. 410 00:22:31,440 --> 00:22:34,520 But at the same time, showing and doing is is more important 411 00:22:34,520 --> 00:22:36,440 there as well. Yeah, showing and doing, I feel 412 00:22:36,440 --> 00:22:40,720 like is is never been as important as right now because I 413 00:22:40,720 --> 00:22:43,600 mean, especially when people are trailblazing when it comes to 414 00:22:44,040 --> 00:22:47,200 developing a product that has an AI functionality or even 415 00:22:47,200 --> 00:22:50,120 building a small agentic system for whatever it is. 416 00:22:50,120 --> 00:22:53,680 I was talking to someone and she said, I have three boys and now 417 00:22:53,680 --> 00:22:56,360 they're at an age where they're all going to tennis tournaments 418 00:22:56,360 --> 00:22:59,320 and they have judo and they have swimming and I need to sign them 419 00:22:59,320 --> 00:23:02,120 up for multiple things. And she just vibe coded some 420 00:23:02,120 --> 00:23:04,400 some agents that do that for her that go through a lot of 421 00:23:04,400 --> 00:23:07,360 websites, give her a summary and then she can execute or then 422 00:23:07,360 --> 00:23:10,680 sometimes even execute all in an automated way. 423 00:23:10,680 --> 00:23:13,160 I was like, that's really cool. It's in essence. 424 00:23:13,160 --> 00:23:16,000 In essence, it doesn't necessarily matter what it is. 425 00:23:16,400 --> 00:23:18,320 I feel like what I want to see is passionate and I don't want 426 00:23:18,320 --> 00:23:20,400 to see people that wait, but I want to see people that have 427 00:23:20,400 --> 00:23:22,440 already started and I can show me something. 428 00:23:22,480 --> 00:23:26,160 Exactly, exactly. Very much so it's, it's about 429 00:23:26,160 --> 00:23:31,480 being able, I mean, there's being being able to even in 430 00:23:31,480 --> 00:23:35,480 terms of just generally A-Team and, and building an, an AI 431 00:23:35,480 --> 00:23:37,840 native. I'm looking at an AI native team 432 00:23:37,840 --> 00:23:40,200 there. The skill set is, is absolutely 433 00:23:40,200 --> 00:23:44,280 paramount and and important there, but the attitudes is, is 434 00:23:44,360 --> 00:23:48,280 even more important there. The ability to be passionate, 435 00:23:48,280 --> 00:23:52,560 have that, that urgency to want to learn, want to do more is 436 00:23:52,560 --> 00:23:54,160 very, very paramount there as well. 437 00:23:54,320 --> 00:23:57,720 And that can easily be shown through those projects and like, 438 00:23:57,720 --> 00:24:00,560 you know, that even vibe coding those, those those as possible 439 00:24:00,560 --> 00:24:04,040 today, even if I have zero to to no experience, that again, is 440 00:24:04,040 --> 00:24:07,400 something to to that can easily be done with these AI 441 00:24:07,400 --> 00:24:09,480 capability. I can generate an entire website 442 00:24:09,480 --> 00:24:12,920 and an entire vibe coded start from zero to 1 iterate. 443 00:24:13,080 --> 00:24:15,880 And with that, I can Start learning and and developing the 444 00:24:15,880 --> 00:24:19,160 parts of, of my understanding of those capabilities. 445 00:24:19,480 --> 00:24:23,880 That essentially is what the market and and enterprises value 446 00:24:23,880 --> 00:24:27,000 a lot today. From your perspective, attitude, 447 00:24:27,200 --> 00:24:30,320 mindset, sense of urgency, are those things that people just 448 00:24:30,320 --> 00:24:32,880 have innately or can they be trained? 449 00:24:33,600 --> 00:24:36,240 And I definitely think those can be trained. 450 00:24:36,760 --> 00:24:43,000 It's #1 not only on in terms of the person, if I take it, for 451 00:24:43,000 --> 00:24:46,280 instance, even with those at, at ServiceNow and, and the team, 452 00:24:46,280 --> 00:24:51,280 they're the, if we have, if there's one I need, if we have a 453 00:24:51,280 --> 00:24:54,440 strong leader, that with a vision. 454 00:24:55,040 --> 00:24:57,160 And then that I'll first talk about it from the leadership 455 00:24:57,160 --> 00:24:59,440 perspective and then from, from a person itself. 456 00:24:59,920 --> 00:25:03,760 But if you have a strong leader with a vision that can go ahead. 457 00:25:03,760 --> 00:25:06,920 And if everyone believes in the mission and, and the vision, 458 00:25:07,360 --> 00:25:09,720 things that you can achieve are, are limitless. 459 00:25:09,720 --> 00:25:12,640 The potential is limitless. And that again comes to, to a 460 00:25:12,920 --> 00:25:16,240 person there as well. If I can lead myself similar to 461 00:25:16,520 --> 00:25:20,520 to what happens in in a team, if I have a mission, if I have a 462 00:25:20,520 --> 00:25:22,600 vision that I deeply believe in there as well. 463 00:25:23,040 --> 00:25:26,240 And that essentially is what can help me iterate and and go 464 00:25:26,240 --> 00:25:28,440 further. How can I train myself or build 465 00:25:28,440 --> 00:25:33,440 that in terms of of a muscle, just just like muscle memory, I 466 00:25:33,440 --> 00:25:35,840 need to build that. Yes, it doesn't come over a day 467 00:25:35,840 --> 00:25:39,160 or or a month, but I need to build that in terms of of a 468 00:25:39,760 --> 00:25:42,960 muscle there and train it. How can I train it and, and 469 00:25:43,040 --> 00:25:46,240 build it? So just what I believe in and 470 00:25:46,240 --> 00:25:50,000 the most important thing is writing things down and here 471 00:25:50,200 --> 00:25:53,440 whenever I whether it's a one year plan or it's three-year 472 00:25:53,440 --> 00:25:56,480 plan, five year plan, just write down whatever you think. 473 00:25:56,920 --> 00:26:00,000 Where are you today? Where could you be tomorrow? 474 00:26:00,840 --> 00:26:04,600 And then in terms of of that tomorrow, do not judge yourself 475 00:26:04,680 --> 00:26:08,080 against today, but judge whenever you have the ability or 476 00:26:08,080 --> 00:26:11,680 something, any action, a decision that you need to take. 477 00:26:11,680 --> 00:26:15,520 Ask yourself what would that tomorrow version of food of me 478 00:26:15,520 --> 00:26:19,400 would would take the OR what that tomorrow version take in 479 00:26:19,400 --> 00:26:23,000 terms of a decision and accordingly take that or or 480 00:26:23,000 --> 00:26:25,880 start shaping yourself and thinking to that as well that 481 00:26:25,960 --> 00:26:29,440 training, training it over time has really helped me there and 482 00:26:29,440 --> 00:26:33,280 be a better person be shaping that urgency that that passion 483 00:26:33,280 --> 00:26:35,960 in me there as well. Just noting things down having 484 00:26:35,960 --> 00:26:39,840 doesn't even have to be a very clear plan, but just a plan. 485 00:26:40,400 --> 00:26:42,320 Where am I today? What am I? 486 00:26:42,520 --> 00:26:46,480 Where could I be tomorrow? And then judging myself by the 487 00:26:46,480 --> 00:26:50,120 tomorrow in terms of every decision is is a big value and 488 00:26:50,120 --> 00:26:51,800 then training it in in terms of muscle. 489 00:26:51,800 --> 00:26:54,440 And then similar to that in terms of of leaders there as 490 00:26:54,440 --> 00:26:58,080 well. Leaders and in in organisations 491 00:26:58,200 --> 00:27:02,840 very important again have strong vision, a mission for my team. 492 00:27:03,120 --> 00:27:07,080 And then based on that, a team strongly believes in in that 493 00:27:07,080 --> 00:27:10,560 mission and that vision limitless potential that that 494 00:27:10,560 --> 00:27:12,320 can be achieved accordingly there. 495 00:27:12,840 --> 00:27:16,480 I like that a lot, acting like future you would or should and 496 00:27:16,480 --> 00:27:18,680 already doing it instead of waiting until you get there. 497 00:27:19,400 --> 00:27:23,600 Exactly, exactly. And sure as well it changes DD 498 00:27:23,600 --> 00:27:28,720 perspective from from judging yourself into which again is 499 00:27:28,720 --> 00:27:32,200 something that could potentially put in in terms. 500 00:27:32,200 --> 00:27:34,640 Of a psychology side of it. Put people off. 501 00:27:35,120 --> 00:27:38,520 You're taking it to what could that other person do? 502 00:27:39,120 --> 00:27:41,520 Which is essentially or the other version of me would do. 503 00:27:41,520 --> 00:27:43,800 And then accordingly, I would, I would take that, that action as 504 00:27:43,800 --> 00:27:45,200 well. So psychologically could also 505 00:27:45,400 --> 00:27:48,280 help or or influence a person to do better. 506 00:27:48,640 --> 00:27:51,600 So that again is is how we've taken it over, at least for 507 00:27:51,600 --> 00:27:54,240 myself, taking it over time and and shape the person I am in 508 00:27:54,240 --> 00:27:57,840 terms of my urgency, my mission, my my passion and my beliefs 509 00:27:57,840 --> 00:27:59,800 are. Yeah, when you were talking 510 00:27:59,800 --> 00:28:02,680 about some of the responsibilities of a for 511 00:28:02,680 --> 00:28:05,280 deployed engineer and especially the ones on the soft skill side. 512 00:28:05,960 --> 00:28:08,560 I've worked in teams where people are just not happy 513 00:28:09,120 --> 00:28:12,800 talking to stakeholders or PMS or being in what they call 514 00:28:12,800 --> 00:28:14,240 meetings and everything is a meeting. 515 00:28:14,240 --> 00:28:17,840 And sometimes back-to-back would just actually absolutely drain 516 00:28:17,840 --> 00:28:19,920 them. And I feel like those people are 517 00:28:19,920 --> 00:28:23,800 not well suited for the forward deployed engineer rule. 518 00:28:23,960 --> 00:28:26,640 I feel like interactions with people, having tough 519 00:28:26,640 --> 00:28:29,720 conversations and getting by and and making sure that you execute 520 00:28:29,720 --> 00:28:33,000 or you're apartment and enabled and equipped to execute, that's 521 00:28:33,040 --> 00:28:35,680 a big part of it. So then I see 2 clear camps, the 522 00:28:35,680 --> 00:28:38,720 people that love let's say the outcome of things and also the 523 00:28:38,720 --> 00:28:40,760 interaction with people. And then people that love tech 524 00:28:40,760 --> 00:28:43,720 for the tech where it's mainly a craft for them as well 525 00:28:43,720 --> 00:28:46,280 specifically. And then one camp can become for 526 00:28:46,280 --> 00:28:51,040 deployed engineers and 11 camp. I don't think can and I don't 527 00:28:51,040 --> 00:28:54,280 know if they should. That's a a very good point. 528 00:28:54,280 --> 00:28:58,520 And indeed, like I said earlier, the soft skills of a typical 529 00:28:58,920 --> 00:29:02,480 software engineer to what is required as as a forward 530 00:29:02,480 --> 00:29:04,800 deployed engineer, yes, they are quite different. 531 00:29:05,240 --> 00:29:09,160 They're I'm expected to to ship products to to write codes and. 532 00:29:10,080 --> 00:29:11,880 Sure. It becomes quite different. 533 00:29:11,880 --> 00:29:15,080 We're working together Co innovating with that customer is 534 00:29:15,080 --> 00:29:16,600 absolutely paramount there as well. 535 00:29:17,080 --> 00:29:22,440 I mean like noted in terms of of AI use cases and the investments 536 00:29:22,440 --> 00:29:24,800 in terms of these projects, they're significant. 537 00:29:25,080 --> 00:29:29,760 So in terms of taking these two to success again is is also very 538 00:29:29,760 --> 00:29:31,720 important there as well. So with that comes different 539 00:29:31,720 --> 00:29:35,480 level sense of pressure, urgency and all those those areas. 540 00:29:35,480 --> 00:29:38,360 So yes, being able to communicate, be in pressure, be 541 00:29:38,360 --> 00:29:43,640 a leader and be able to, to take that to, to successes, be driven 542 00:29:43,640 --> 00:29:45,800 to take that to, to successes is important. 543 00:29:46,200 --> 00:29:50,800 And yeah, even in, in those pressure environments, those 544 00:29:51,120 --> 00:29:54,600 communicating with people, those are again, if it's not innately 545 00:29:54,600 --> 00:29:58,240 in, in, in a person, then those, those do need to to be 546 00:29:58,240 --> 00:30:00,320 developed. I mean, if I take it from my 547 00:30:00,320 --> 00:30:04,200 perspective as well, I'm actually an an introverted 548 00:30:04,200 --> 00:30:08,680 person, but I started at NASA when I was a researcher and AI 549 00:30:08,680 --> 00:30:11,000 researcher initially in in my career. 550 00:30:11,520 --> 00:30:14,800 I would much prefer to just sit behind the laptop, call out, 551 00:30:14,800 --> 00:30:17,120 innovate, do research and I'm. Quite a happy. 552 00:30:17,120 --> 00:30:21,920 Person and then what changed me essentially I took a sales role, 553 00:30:21,920 --> 00:30:26,440 challenged myself, took a a sales role in in the UK. 554 00:30:26,440 --> 00:30:28,600 There it was door to door used to do engineering. 555 00:30:28,960 --> 00:30:32,240 It was internship engineering in the morning, then in the evening 556 00:30:32,240 --> 00:30:35,080 used to do door to door sales. Proper sales. 557 00:30:35,160 --> 00:30:38,440 Proper sales, door to door. And then there's, you literally 558 00:30:38,480 --> 00:30:41,160 have, we have something called law of averages, which is the 559 00:30:41,160 --> 00:30:44,600 number of people you need to speak to before you you can make 560 00:30:44,600 --> 00:30:47,840 a sale sale essentially. So the averages usually averages 561 00:30:47,840 --> 00:30:50,880 to three to five people that you sell when you speak to 100 562 00:30:50,880 --> 00:30:52,160 people. So. 563 00:30:52,440 --> 00:30:53,480 Oh, wow. Exactly. 564 00:30:53,480 --> 00:30:56,600 Yeah. So imagine knocking 100 doors at 565 00:30:56,600 --> 00:30:59,520 the minimum a day in order to make two to five sales and you 566 00:30:59,520 --> 00:31:03,560 get around 95 to 97 rejections a day. 567 00:31:03,560 --> 00:31:07,760 So that's quite it shapes up or change the person quite a lot 568 00:31:07,760 --> 00:31:10,760 and that is essentially what would got me or help me get get 569 00:31:10,760 --> 00:31:13,840 myself out of my shell. So I would say yes, although the 570 00:31:13,840 --> 00:31:17,640 person could and for me, I still today prefer to, to be 571 00:31:17,640 --> 00:31:21,360 introvert, but that definitely changed how I communicate, how I 572 00:31:21,360 --> 00:31:25,200 interact with people, how I can easily or be able to, to speak 573 00:31:25,200 --> 00:31:28,920 to to anyone, accommodate to a situation and and be able to be 574 00:31:28,920 --> 00:31:33,000 comfortable with being my myself and sharing my ideas there as 575 00:31:33,000 --> 00:31:35,720 well. So that I think although it's 576 00:31:35,720 --> 00:31:39,960 not in Italy there it can be can be trained or got in terms of of 577 00:31:40,040 --> 00:31:42,840 a person there as well. Yeah, I feel like you've truly 578 00:31:42,840 --> 00:31:44,800 put in the reps. Like you don't just get where 579 00:31:44,800 --> 00:31:47,040 you you want to be. You have to put in reps and you 580 00:31:47,040 --> 00:31:50,200 do that day in, day out, and that's how you grow innocence. 581 00:31:50,480 --> 00:31:52,680 You mentioned NASA. I want to do a quick detour 582 00:31:52,680 --> 00:31:55,560 because all the people that I spoke to where I said I'm going 583 00:31:55,560 --> 00:31:57,960 to talk to MO, he's been to NASA for an internship. 584 00:31:57,960 --> 00:32:01,400 Everyone's like, whoa, how cool is NASA from behind the scenes, 585 00:32:01,400 --> 00:32:03,640 from your experience it? Was really, really cool. 586 00:32:04,280 --> 00:32:09,000 So I went there early and I was only 16 when when I went there, 587 00:32:09,000 --> 00:32:14,920 but it was really interesting and, and what's pinned up my 588 00:32:14,920 --> 00:32:16,720 passion and, and AI there as well. 589 00:32:17,160 --> 00:32:21,320 So essentially I went there and started as was, it was an 590 00:32:21,320 --> 00:32:25,560 exchange program where we're essentially researching about 591 00:32:26,040 --> 00:32:28,880 renewable energy, how we can optimize those systems and how 592 00:32:28,880 --> 00:32:34,200 we can use AI to that as well. Here solar panels was the main 593 00:32:34,200 --> 00:32:38,200 study and there how we can use AI to prove it was was again a 594 00:32:38,200 --> 00:32:43,360 big area amazing experience again in terms of working with 595 00:32:43,440 --> 00:32:46,960 some of the greatest minds that in the world that you have there 596 00:32:47,000 --> 00:32:51,720 the access there and then be able to to help the field and 597 00:32:51,720 --> 00:32:55,240 and move the field ahead. As was essentially how what got 598 00:32:55,240 --> 00:32:58,400 me like I noted in all of this, and it was amazing as as an 599 00:32:58,400 --> 00:33:02,640 experience at was at Kennedy Space Center in in Florida. 600 00:33:03,600 --> 00:33:08,600 Again, beautiful place as well. And essentially partly in 601 00:33:09,400 --> 00:33:13,160 there's one side of NASA there as well in just a reserve. 602 00:33:13,600 --> 00:33:16,680 So again, normally do you can you work with the greatest 603 00:33:16,680 --> 00:33:20,000 minds, but you also have just animals that are roaming free as 604 00:33:20,000 --> 00:33:22,000 well around. It's just beautiful scenes, 605 00:33:22,200 --> 00:33:26,320 beautiful mud place people to or beautiful places as well as 606 00:33:26,480 --> 00:33:28,920 amazing people to, to work with there as well. 607 00:33:29,760 --> 00:33:34,880 I did stay or there around around a year before I, I moved, 608 00:33:35,200 --> 00:33:38,400 I had to, to the UK and again, was in the research side of it 609 00:33:38,400 --> 00:33:41,840 of, of AI there. And again, look at how we can 610 00:33:41,840 --> 00:33:46,520 apply AI, whether it's in in Kenya and and other places to, 611 00:33:46,520 --> 00:33:51,200 to optimise their end to end electricity provisioning systems 612 00:33:51,200 --> 00:33:53,920 and, and improve that. And again, that's essentially 613 00:33:53,920 --> 00:33:56,480 working in all. One thing about AI, which I love 614 00:33:56,480 --> 00:34:01,000 was is how, whether at NASA or all these different places, you 615 00:34:01,000 --> 00:34:04,000 can apply it in every fields. You can apply it in every field 616 00:34:04,000 --> 00:34:05,720 and it can bring a lot of value there as well. 617 00:34:05,960 --> 00:34:08,960 That's again, another reason why if you're looking at it from a 618 00:34:08,960 --> 00:34:11,440 perspective of software engineer, what can be very 619 00:34:11,440 --> 00:34:14,639 appealing, Yes, even engineering, I can essentially 620 00:34:14,639 --> 00:34:18,719 create an app or develop a, a solution for in any fields and, 621 00:34:18,719 --> 00:34:21,679 and to support it. But again, that is further 622 00:34:21,679 --> 00:34:25,840 augmented and further expanded when when it comes to AI there 623 00:34:25,840 --> 00:34:27,639 as well. That's again something I learnt 624 00:34:28,040 --> 00:34:31,280 very early at at NASA as well. The ability I can apply AI in 625 00:34:31,280 --> 00:34:34,199 anything and it's, it's something that can potentially 626 00:34:34,199 --> 00:34:36,760 get get transformational value. Obviously, at that time, it 627 00:34:36,760 --> 00:34:41,280 wasn't as as big or as around 810 years ago. 628 00:34:41,360 --> 00:34:47,480 So it was, it's AI wasn't as as big today at all as as it is. 629 00:34:47,480 --> 00:34:50,360 But still at that time there were early innings and it was 630 00:34:50,360 --> 00:34:54,040 clear that it was big potential in in the field there as well. 631 00:34:54,159 --> 00:34:55,840 Yeah. One of the things that you 632 00:34:55,840 --> 00:34:58,440 mentioned specifically the reason why for deployed 633 00:34:58,440 --> 00:35:03,040 engineering jobs are booming and growing and hiring specifically 634 00:35:03,040 --> 00:35:06,400 and also people applying to them is because we are trailblazing 635 00:35:06,400 --> 00:35:08,880 with new technologies where companies and a lot of tools are 636 00:35:08,880 --> 00:35:11,600 out there. I mean as a software engineer, I 637 00:35:11,600 --> 00:35:15,160 get bombarded with tooling and from a marketing perspective 638 00:35:15,160 --> 00:35:17,880 mainly. So then actually applying it in 639 00:35:17,880 --> 00:35:21,680 a production level system, specifically enterprise but also 640 00:35:21,680 --> 00:35:23,680 user facing, that's the challenge. 641 00:35:23,680 --> 00:35:25,520 And that's where for deployed engineers will help. 642 00:35:25,920 --> 00:35:28,560 But then does that mean that when this technology becomes a 643 00:35:28,560 --> 00:35:30,800 little bit more stable and people are a little bit more 644 00:35:30,800 --> 00:35:34,240 apartment with what they do, that for deployed engineer jobs 645 00:35:34,240 --> 00:35:37,360 will go down or will be reduced or will go the knowledge will go 646 00:35:37,360 --> 00:35:40,320 to organizations themselves? How do you see that? 647 00:35:40,520 --> 00:35:47,680 Great question, SO. Number one in terms of AIAI is 648 00:35:47,680 --> 00:35:50,840 still very, very early and I think it's early innings. 649 00:35:51,360 --> 00:35:56,280 So I mean, you hear in the market these days, is there a 650 00:35:56,280 --> 00:35:59,160 bubble? So I'd I'd say there's 651 00:35:59,200 --> 00:36:02,600 absolutely no bubble at all because it's very, very early 652 00:36:02,600 --> 00:36:07,440 innings #1 if you look at AI is. Bigger than all the previous. 653 00:36:08,240 --> 00:36:11,920 Whether you take the Internet, whether you take mobile clouds, 654 00:36:11,920 --> 00:36:14,520 combine them together, you know is is bigger than them in terms 655 00:36:14,520 --> 00:36:19,400 of the, the opportunity there #2 why is it early innings? 656 00:36:19,400 --> 00:36:21,520 If if you look at the entire Internet and how it is 657 00:36:21,520 --> 00:36:25,480 commoditized, the app, the ads and all of that, that's all 658 00:36:25,480 --> 00:36:27,960 today done. The recommended systems that's 659 00:36:27,960 --> 00:36:31,880 all done today through, through just the, the machine learning 660 00:36:31,880 --> 00:36:36,600 workloads there all that needs to be taken away, architected 661 00:36:36,600 --> 00:36:38,880 and transitioned into generative AI workloads. 662 00:36:39,240 --> 00:36:43,760 Take companies, every company in the world essentially that needs 663 00:36:43,760 --> 00:36:46,680 to, to get that edge and and compete. 664 00:36:46,680 --> 00:36:51,480 There is already adopting AI And today we see it moving already 665 00:36:51,480 --> 00:36:54,280 from mainly from PO CS to, to production and, and they're 666 00:36:54,280 --> 00:36:57,920 scaling out their usage. So it's very, very early innings 667 00:36:57,960 --> 00:36:59,960 for AI and, and the opportunity there. 668 00:37:00,760 --> 00:37:05,400 So in terms of the, the, the, the potential rise of, of the 669 00:37:05,400 --> 00:37:08,880 role, it's, it's going, I'm pretty sure it's going to be 670 00:37:08,880 --> 00:37:13,720 quite exponential for, for the coming decades there to when you 671 00:37:13,720 --> 00:37:19,560 look at EI itself and the pace of, of innovation beats any 672 00:37:19,560 --> 00:37:22,160 other technology that are around as well. 673 00:37:22,640 --> 00:37:25,520 Now we have models that are coming about every other day, 674 00:37:25,840 --> 00:37:30,160 whether if, if you look at it from the, the closed source or 675 00:37:30,160 --> 00:37:33,520 even open source perspective, just DeepSeek just released a 676 00:37:34,120 --> 00:37:38,280 new model there as well, new in terms of new paradigms that it 677 00:37:38,280 --> 00:37:40,480 uses in the RL side of it as well. 678 00:37:40,680 --> 00:37:44,160 So we can still see not only that there is potential in terms 679 00:37:44,160 --> 00:37:47,400 of scaling when I when you look at it in the, the RLM side, 680 00:37:47,400 --> 00:37:49,760 there's there's potential for scaling in the pre training, 681 00:37:49,760 --> 00:37:52,680 There's potential for scaling in the, the post training aside of 682 00:37:52,680 --> 00:37:54,040 it. And there's also the, the 683 00:37:54,040 --> 00:37:59,040 potential for still new recipes, new innovations and inventions 684 00:37:59,240 --> 00:38:03,600 that can help or help take the, the AI to the, to the next step 685 00:38:03,600 --> 00:38:05,800 there as well to general and, and super intelligence. 686 00:38:06,240 --> 00:38:10,480 So opportunity again with AI, not really is it huge for, for 687 00:38:10,480 --> 00:38:14,680 the the entire market and still earnings, but self AI field is, 688 00:38:14,880 --> 00:38:18,560 is rapidly innovating and the pace is, is much faster. 689 00:38:18,840 --> 00:38:24,520 So you put those two together is is it going is, is there 690 00:38:24,520 --> 00:38:27,640 potential for it to to decrease or or plateau? 691 00:38:27,920 --> 00:38:32,480 I don't see that potentially for for the next decade or so until 692 00:38:32,760 --> 00:38:36,600 and when we get to general and super intelligence in in the the 693 00:38:36,600 --> 00:38:39,360 next decade. And you can imagine every 694 00:38:39,360 --> 00:38:43,280 organization our have the potential to go ahead and create 695 00:38:43,280 --> 00:38:47,000 new products to do discovery, drug discovery and and medical 696 00:38:47,000 --> 00:38:49,000 discovery. There I'm able to to go ahead 697 00:38:49,000 --> 00:38:52,120 and transform an entire organization and manage 698 00:38:52,120 --> 00:38:56,000 organizations autonomously. That is completely different 699 00:38:56,000 --> 00:38:58,480 paradigm there. And that again becomes 700 00:38:58,600 --> 00:39:01,600 absolutely paramount, even more paramount to have those those 701 00:39:01,600 --> 00:39:03,840 forward deployed engineers there as well. 702 00:39:03,840 --> 00:39:08,400 People who innately understand those those products and are 703 00:39:08,520 --> 00:39:13,120 also are able to understand the the customer, the customers, the 704 00:39:13,240 --> 00:39:16,240 architecture, their systems and their processes in order to be 705 00:39:16,240 --> 00:39:18,960 able to to rewire them there again for for the future. 706 00:39:19,640 --> 00:39:24,200 And finally, as as a point in, in terms of AI today, there's, 707 00:39:24,560 --> 00:39:28,920 it's all mainly based were already based on, on today's 708 00:39:29,160 --> 00:39:33,920 current technologies, the likes of the GPUs and Nvidia's or, or 709 00:39:33,920 --> 00:39:37,040 any one of the the latest CMD and all the latest GPUs and, and 710 00:39:37,040 --> 00:39:39,240 E6 there. But again, there's quantum 711 00:39:39,240 --> 00:39:41,600 computing that potentially can come about there as well. 712 00:39:42,040 --> 00:39:46,040 And just a few days back, IBM, there was a video on IBM and 713 00:39:46,040 --> 00:39:50,800 Bloomberg on, on the potential of, of their, their quantum 714 00:39:50,800 --> 00:39:54,960 computers there as well. Bring that field again into AI. 715 00:39:55,160 --> 00:39:59,200 And there is even more rapid innovation and and iteration 716 00:39:59,200 --> 00:40:02,040 that can come into market when we have all these, these large 717 00:40:02,040 --> 00:40:06,720 scale AI factories that we're right now just building up for, 718 00:40:06,960 --> 00:40:09,640 for these fields is that I can again reshape at all. 719 00:40:09,640 --> 00:40:14,680 So I do not foresee a plateau at least in in the near future when 720 00:40:14,680 --> 00:40:18,480 I put put those all together. Yeah, One of the last thoughts I 721 00:40:18,480 --> 00:40:21,720 had, it brings me back to an experience that I had also quite 722 00:40:21,720 --> 00:40:25,760 early in career because I went to a training training to 723 00:40:25,760 --> 00:40:28,880 actually demo a local platform. The training was to figure out 724 00:40:29,120 --> 00:40:31,640 how to then use that and implement that at customers. 725 00:40:31,960 --> 00:40:34,640 And at the time I decided I don't want to be a local 726 00:40:34,640 --> 00:40:36,520 engineer. I felt like if I go this route 727 00:40:36,520 --> 00:40:40,040 in my career, I would be very much tied to a technology and 728 00:40:40,040 --> 00:40:42,880 yes, I would understand customer domains and I would implement, 729 00:40:42,880 --> 00:40:46,480 but my technical expertise would be tied to this one thing. 730 00:40:46,480 --> 00:40:49,200 And if that one thing dies, well I have to build up another 731 00:40:49,200 --> 00:40:51,040 technical capability from scratch. 732 00:40:51,640 --> 00:40:53,800 Do you see the same risk with forward deployed engineers? 733 00:40:53,800 --> 00:40:56,520 Like what makes people for deployed engineers different 734 00:40:56,520 --> 00:40:59,560 from Palantir versus service now with the technical expertise 735 00:40:59,560 --> 00:41:03,520 they have? Now the hard skills if if we 736 00:41:03,520 --> 00:41:06,960 take those are typical across all organisations there as well. 737 00:41:07,240 --> 00:41:11,200 So if the multi cloud technologies, the the below it, 738 00:41:11,200 --> 00:41:14,920 the Pythons, the the type space, the you're taking it from the 739 00:41:14,920 --> 00:41:18,840 the container side of it, all of that is typical and and the 740 00:41:18,840 --> 00:41:21,360 same. I need to know all those hard 741 00:41:21,360 --> 00:41:24,760 skills is what I'm saying are are typically what are any 742 00:41:24,760 --> 00:41:27,680 organization that I go to, I will still need to go ahead and 743 00:41:27,680 --> 00:41:30,240 apply those. What is different is essentially 744 00:41:30,240 --> 00:41:34,280 the product and how I can I can go ahead and take that, innovate 745 00:41:34,280 --> 00:41:37,000 it or or customize that according to to the customer 746 00:41:37,000 --> 00:41:40,600 objectives there. So what is more important is 747 00:41:40,600 --> 00:41:45,840 being adaptable in terms of of in the, the space of AI like I 748 00:41:45,840 --> 00:41:47,960 noted before, AI can be applied in any field. 749 00:41:48,360 --> 00:41:52,120 So whether I apply it in Palantir or ServiceNow or or any 750 00:41:52,120 --> 00:41:55,840 other organization, it's still going to be AI there. 751 00:41:56,120 --> 00:42:00,200 And then in terms of the the agentic and the the latest 752 00:42:00,200 --> 00:42:03,880 innovations in market there with the capabilities, yes, there are 753 00:42:03,880 --> 00:42:06,000 differentiators. For instance, if we take it away 754 00:42:06,000 --> 00:42:09,040 at ServiceNow with, with our capabilities, if you take the 755 00:42:09,040 --> 00:42:11,840 gardener, the OSG reports we're market leading and we have we're 756 00:42:12,280 --> 00:42:14,800 number one for instance on, on gardeners building and managing 757 00:42:14,840 --> 00:42:18,240 AI agent use cases. How are we able to to achieve 758 00:42:18,240 --> 00:42:22,880 that is can we have our, our, the ability to rapidly take not 759 00:42:22,880 --> 00:42:25,880 only just off the off the shelf solution, but I can go ahead 760 00:42:26,160 --> 00:42:29,600 take it or customize that very easily and and be able to to 761 00:42:29,920 --> 00:42:32,760 take it to production. Now those tweaks or those 762 00:42:33,040 --> 00:42:36,480 differentiators are going to be in, in every product or every 763 00:42:36,480 --> 00:42:39,600 enterprise that a person might, might potentially work in. 764 00:42:39,960 --> 00:42:45,680 But that is it's essentially an add on, not the core part of or 765 00:42:45,680 --> 00:42:48,240 the course skill of of the person that they will have, 766 00:42:48,400 --> 00:42:51,120 especially in an engineering or or an AI field there as well. 767 00:42:51,360 --> 00:42:57,400 So I would say if one has like noted earlier, learn learning by 768 00:42:57,400 --> 00:43:00,880 doing and then showing versus just going to to take the 769 00:43:00,880 --> 00:43:04,480 traditional path and then accordingly building up those 770 00:43:04,480 --> 00:43:08,880 hard skills and being able to rapidly build those those up as 771 00:43:08,920 --> 00:43:11,800 as well. And then third again, when once 772 00:43:11,800 --> 00:43:15,040 I have that the add on is essentially the the product to 773 00:43:15,040 --> 00:43:17,120 where I'm going to, to work on there as well. 774 00:43:17,120 --> 00:43:20,560 And I'm very privileged just to work at service now in order to 775 00:43:20,560 --> 00:43:23,320 be, to be able to, to do that for some of the leading 776 00:43:23,320 --> 00:43:25,200 enterprises in the world. Yeah, amazing. 777 00:43:25,320 --> 00:43:27,520 I like that a lot. For people listening, they will 778 00:43:27,520 --> 00:43:30,840 now have a better understanding of what the forward deployed 779 00:43:30,840 --> 00:43:34,840 engineer is both in a role and execution, as well as the skills 780 00:43:34,840 --> 00:43:36,480 and potentially even how to get there. 781 00:43:36,640 --> 00:43:39,640 Get a foot in the door. What is one last piece of advice 782 00:43:39,720 --> 00:43:41,000 you want to leave people off with? 783 00:43:42,440 --> 00:43:46,520 Wait, let's say. So we, we've talked about how 784 00:43:46,520 --> 00:43:48,720 the market and how it's rapidly changed. 785 00:43:49,200 --> 00:43:55,720 Like noted from if you take AQ one to QQ 123 to Q125, the 786 00:43:55,720 --> 00:43:59,400 software engineering jobs dropped by 70%, white collar by 787 00:43:59,400 --> 00:44:03,840 just around 36. If you take the AIFFD year olds 788 00:44:04,320 --> 00:44:11,000 eight, 800,000 or 800% to 1000%. There signal is very, very clear 789 00:44:11,000 --> 00:44:15,800 in the markets #2 what the market values is different 790 00:44:16,000 --> 00:44:20,080 skills are much more important. So learning again, learning by 791 00:44:20,080 --> 00:44:23,200 doing and being able to, to show that is, is absolutely paramount 792 00:44:23,200 --> 00:44:27,320 there as well. And #3 it can, yes, it can be 793 00:44:27,320 --> 00:44:31,040 daunting as in in terms of what is or the the requirements 794 00:44:31,040 --> 00:44:34,560 there. But don't shy away as we talked 795 00:44:34,560 --> 00:44:37,800 about the value, the potential and the opportunity is 796 00:44:37,800 --> 00:44:43,160 significant and even like noted in terms of the could it 797 00:44:43,160 --> 00:44:44,160 potentially. Plateau. 798 00:44:44,680 --> 00:44:48,360 With all those points that I I provided, there is little to to 799 00:44:48,680 --> 00:44:50,480 no chance in, in my belief there. 800 00:44:50,840 --> 00:44:54,280 Put those all together, it is a career that is definitely worth 801 00:44:54,280 --> 00:44:58,200 to go ahead with and into, especially in the the AI side of 802 00:44:58,200 --> 00:45:01,760 it and being able to combine it with that engineering take 803 00:45:01,760 --> 00:45:05,360 organisations forward. I feel again, challenging 804 00:45:05,360 --> 00:45:09,800 yourself and growing with the the ability of AI and where it 805 00:45:09,800 --> 00:45:13,000 can go, the opportunity, the opportunity of AI and where it 806 00:45:13,000 --> 00:45:16,920 can go is quite significant. So take it start, don't try 807 00:45:16,920 --> 00:45:18,760 always is what I would say there. 808 00:45:19,080 --> 00:45:21,120 MO thank you so much, I really enjoyed this. 809 00:45:21,120 --> 00:45:22,360 It's been an absolute pleasure, Patrick. 810 00:45:22,360 --> 00:45:23,600 Thank you very much. Thank you. 811 00:45:24,000 --> 00:45:25,840 If you're still listening, let us know in the comments section 812 00:45:25,840 --> 00:45:27,960 what you think and we'll see you in the next episode.