How to Become a $300K AI Engineer in 2026 (Complete Roadmap) — Transcript

Full transcript

1. 0:00AI engineers are making over $300,000
2. 0:02every single year. And especially at top
3. 0:05tech companies, that number could get
4. 0:06even crazier going over $1 million.
5. 0:09[music] And so, if you want to become
6. 0:10that rich AI engineer, this is the video
7. 0:13for you. If you're new here, hi. My name
8. 0:15is Sajad. I got my master's in AI and
9. 0:17computer science from Georgia Tech when
10. 0:18I was 20. I worked at big tech companies
11. 0:20like Amazon, and I interviewed popular
12. 0:23tech CEOs to get a real insider scoop on
13. 0:25the future of AI. And in this video, I'm
14. 0:27going to give you the exact four-level
15. 0:29AI engineer stack that you need to land
16. 0:31your first $300,000 AI engineer job.
17. 0:34Even if you have no experience
18. 0:35whatsoever, it's completely fine. I'm
19. 0:37going to take you from the absolute
20. 0:39fundamentals of AI engineering all the
21. 0:40way to actually getting that job with
22. 0:42specific resources, skills, and projects
23. 0:44you can start doing today. And so, if
24. 0:46you're ready for this AI engineer
25. 0:48roadmap masterclass, let's get into it
26. 0:50right away. The first block on our AI
27. 0:52engineer stack is our foundations. And
28. 0:54most people will skip this part cuz it's
29. 0:55not the exciting AI ML stuff, but this
30. 0:58stuff is actually so, so important. So,
31. 1:00please slow down. And there are actually
32. 1:01three skills I want you to focus on in
33. 1:03this section. The first skill is
34. 1:04learning Python, the coding language.
35. 1:07And the reason is that pretty much every
36. 1:09AI machine learning tool on the planet
37. 1:11lives in Python. PyTorch, the OpenAI
38. 1:13SDK, Hugging Face, LangChain, they all
39. 1:15speak Python first, so it is the
40. 1:17language the entire field is built on.
41. 1:19Now, when you are learning this
42. 1:20language, it is very important that you
43. 1:21do not fall into tutorial hell. I went
44. 1:24through this a lot where I would just
45. 1:25watch tutorial after tutorial thinking I
46. 1:27was learning, but you do not learn until
47. 1:30you start building. So, stop consuming
48. 1:32and start creating. The second skill you
49. 1:34must master is SQL. And the reason for
50. 1:36that is because AI runs on data. The AI
51. 1:39model is only as good as the data you
52. 1:41feed it. And the programming language of
53. 1:43data is SQL. It is how you retrieve data
54. 1:45from databases and actually shape it
55. 1:47into something you can use. And compared
56. 1:49to everything else I'm going to talk
57. 1:51about in this video, it's probably the
58. 1:52easiest skill you're going to pick up.
59. 1:54But, if you are curious at basics, you
60. 1:56should know at least these three. Select
61. 1:59is used to pull the data you want. Where
62. 2:01is used to filter down the data. And
63. 2:03joins is used to combine data across
64. 2:05three tables. These three are
65. 2:06cornerstone commands for SQL, so use
66. 2:08them a lot. Put them in your projects,
67. 2:10and just like that, slowly but surely,
68. 2:12over time you will become a solid SQL
69. 2:15coder. The third foundational skill to
70. 2:16master is API architecture. And this is
71. 2:19huge for software engineers, and
72. 2:21moreover now for AI engineers, too.
73. 2:23Think of API architecture kind of like
74. 2:25how a bank works. A bank contains vaults
75. 2:28where it stores millions and millions of
76. 2:29dollars, and you, as a regular person,
77. 2:32cannot just walk into the vaults and
78. 2:33take the money out. Instead, you go to
79. 2:36an ATM, which is a controlled window
80. 2:38where you make your request, and then
81. 2:40you receive your cash. Similarly,
82. 2:42software works the same exact way. When
83. 2:44amazon.com wants to retrieve your
84. 2:46customer order, it doesn't go into the
85. 2:48database directly. It communicates
86. 2:50through the API. It submits a request,
87. 2:52receives the data, and then presents it
88. 2:54on the screen. And as an AI engineer,
89. 2:56you will regularly be working with APIs,
90. 2:59sending a request to OpenAI or
91. 3:00Anthropic's APIs to actually access
92. 3:02their models. So, make sure you master
93. 3:04this. Moving on to the second block of
94. 3:06our AI engineering stack, it's time to
95. 3:08learn how to harness the AI models. We
96. 3:10got our foundation solid, and now we
97. 3:12must actually start learning and
98. 3:14building with AI. And I have a simple
99. 3:16framework called CAM to help you
100. 3:18remember this.
101. 3:21C is for context engineering, and this
102. 3:24is the most underrated skill in this
103. 3:25whole section. a lot of people just
104. 3:27focus on basic prompt engineering, like
105. 3:30obsessing over how to word the question
106. 3:31they ask the model to get the most out
107. 3:33of it. But, if you learn how to set up
108. 3:35the entire context your AI model
109. 3:37operates in, you get ahead of almost
110. 3:39everyone. Maybe you learn how to build a
111. 3:41system prompt for tasks you run over and
112. 3:44over again, because that's exactly what
113. 3:45your model needs to be efficient in
114. 3:47knowing what you expect every time. Or,
115. 3:50even better than that, maybe build a
116. 3:51master prompt. So, the AI knows exactly
117. 3:54who you are. Are you a student? Are you
118. 3:56a seasoned engineer? Are you a
119. 3:58researcher? Develop a master prompt, put
120. 4:00it into your AI, and you'll see it do
121. 4:02wonders. Because everyone knows how to
122. 4:04use AI in a general sense. But, if you
123. 4:06want to be an AI engineer and get paid
124. 4:09top dollar, you actually have to know
125. 4:10how to shape AI for your specific needs.
126. 4:13The whole secret is in the context that
127. 4:15you engineer. A is for AI APIs, mainly
128. 4:19the OpenAI and Anthropic ones. So, you
129. 4:21already know what an API is from block
130. 4:23one. But now, you have to put it into
131. 4:25practice by putting that skills straight
132. 4:28at the AI models. And the best way to
133. 4:30start is with your own practice project,
134. 4:32because nothing teaches you like
135. 4:33building. What I would do is drop a
136. 4:35block of text into a note file, then
137. 4:37write a bit of Python code that reads
138. 4:39that file, parses the text, and sends it
139. 4:41into the OpenAI API to get back a clean
140. 4:44three-sentence summary. From there, you
141. 4:46can push it further, like having it
142. 4:47translate the text into another
143. 4:49language, or pulling out key action
144. 4:51items. [music] And as you practice
145. 4:52prompting and shaping the context
146. 4:54through these APIs, you will get
147. 4:56stronger at both API usage and AI usage.
148. 5:00To take it up a notch, we have M, and
149. 5:02that is for models. I want you to get
150. 5:04into the practice of running your own
151. 5:06models. And the best place to start is
152. 5:08Hugging Face, which is a giant library
153. 5:11of free pre-trained models you can
154. 5:12download and run on your own machine.
155. 5:14You can find a model that is good for
156. 5:16image generation, text classification,
157. 5:18or even 3D modeling. Ultimately, just
158. 5:20download one and put it into practice
159. 5:22across different use cases, because that
160. 5:24is how you actually build real knowledge
161. 5:26and skill as an AI engineer. Now,
162. 5:28everything from here in level two, with
163. 5:30harnessing the models, and then
164. 5:32ultimately getting to the point where
165. 5:33you can ship real AI systems, is exactly
166. 5:36where DataCamp took me from knowing the
167. 5:38concepts to actually building them. I've
168. 5:40used DataCamp for a while to level up my
169. 5:42own skills. And their Associate AI
170. 5:44Engineer for Developers track maps
171. 5:46almost one-to-one onto this stack. The
172. 5:48Open AI and Anthropic APIs, Hugging
173. 5:51Face, LangChain, vector databases,
174. 5:53prompt and context engineering, and the
175. 5:55LLM op side of deploying real systems.
176. 5:58The free tools I mentioned, Hugging
177. 5:59Face, the GitHub repo, are great to
178. 6:01practice alongside it. And the track is
179. 6:03what strings them into a real end-to-end
180. 6:05skill set instead of scattered
181. 6:06experiments. Plus, it's all hands-on.
182. 6:08You build real projects you can put
183. 6:10straight on your resume. And when you're
184. 6:12ready to prove it, their AI Engineer for
185. 6:13Developers Association certification is
186. 6:16a credential you can put on LinkedIn,
187. 6:18which matters more every month as this
188. 6:19field gets competitive. Plus, their
189. 6:21Python Programming Fundamentals track
190. 6:23and SQL Fundamentals track are perfect
191. 6:25for those who want to learn. It's fully
192. 6:27interactive, every concept is a
193. 6:29bite-size lesson you apply in the
194. 6:30browser straight away, so you're writing
195. 6:32real code from lesson one instead of
196. 6:34just watching. If you want to get
197. 6:35started, all tracks are linked down
198. 6:37below for you to use. So now, at this
199. 6:39point, you will actually have basic AI
200. 6:41engineering skills. But to actually take
201. 6:43it to the next level, where you can
202. 6:44create end-to-end AI projects, that's
203. 6:47where we move to level three, shipping
204. 6:49AI systems. And there are four skills
205. 6:51here to focus on. The first skill you
206. 6:53must master is RAG, or Retrieval
207. 6:56Augmented Generation. So, typically,
208. 6:58models like ChatGPT or Claude know
209. 7:00everything that exists out there on the
210. 7:02internet. But what if you work for a
211. 7:03private company and it has its own data
212. 7:06or specific policies? ChatGPT has no
213. 7:08idea what it is. So, you need to set up
214. 7:10your own RAG pipeline if you want an AI
215. 7:12model to know your specific data. And
216. 7:15so, here's how it works. First, you take
217. 7:17all your data and your documentations
218. 7:19and break it into small chunks. Each
219. 7:20chunk turns into an embedding, which is
220. 7:22just numbers that are used for the AI
221. 7:24models to read through. Those embeddings
222. 7:26get stored in a vector database.
223. 7:27Ultimately, when someone asks a
224. 7:29question, this is the retrieval part,
225. 7:31the system searches the database and
226. 7:33pulls out the relevant chunks for that
227. 7:34set question. And it does augmentation
228. 7:37to that prompt. Then, ultimately, it
229. 7:38does the generation, it gives you the
230. 7:40answer using the context that has,
231. 7:42though, and gives the user what they
232. 7:43want. The second skill you must master
233. 7:45is agents. This is where the entire AI
234. 7:48industry is sprinting right now. So,
235. 7:50most people used to use AI by prompting
236. 7:52ChatGPT and asking it to give you some
237. 7:54code. But, now with agents, you have
238. 7:57mini workers for you, which amplifies
239. 8:00this whole thing. It can do your whole
240. 8:01coding project end to end. It can read
241. 8:03through documentation, write all the
242. 8:05code for you, review all the code for
243. 8:06you, and add test cases for you. Each
244. 8:08agent is in charge of specific tasks,
245. 8:10instead of you having to prompt ChatGPT
246. 8:13one by one. And then, you can kick it up
247. 8:15a notch with MCP, your model context
248. 8:17protocol, which gives your agents access
249. 8:19to a whole bunch of apps. For example,
250. 8:21once your agents finish the code, they
251. 8:23could connect GitHub through MCP, push
252. 8:26everything to your repo, and open a pull
253. 8:28request for your team to review, and
254. 8:29message your teammates on Slack. They
255. 8:31can do all of this for you without you
256. 8:33having to lift a single finger. And as
257. 8:36you continue to develop more and more
258. 8:37projects, this naturally leads into the
259. 8:40third skill you must master, and that is
260. 8:42deployment. Because creating projects is
261. 8:44cool, but it doesn't really matter
262. 8:46unless it's out there for everyone to
263. 8:47see. And so, you must learn Docker,
264. 8:50because this tool will pack your code,
265. 8:52dependencies, and your model into a
266. 8:54container, which you can then take and
267. 8:56put on AWS or GCP, which allows you to
268. 8:59effectively host your code for the whole
269. 9:01world to see. And especially if you want
270. 9:03to get hired, learning Docker, AWS, and
271. 9:06GCP will increase your chances by so
272. 9:08much, because so few people actually
273. 9:10spend the time to learn it well. Matter
274. 9:12of fact, from my own experience during
275. 9:14an interview, I landed that job just by
276. 9:16walking the recruiter through all the
277. 9:18different AWS technologies I knew,
278. 9:19because I knew that was going to impress
279. 9:21literally anyone and easily set me
280. 9:23apart. The fourth skill you must master
281. 9:25is evaluation and governance. So, once
282. 9:28your system is live, you have to keep it
283. 9:30reliable and keep it under control.
284. 9:32Evaluation is how you keep it reliable,
285. 9:34because you You improve what you do not
286. 9:36measure. A framework like deep eval lets
287. 9:39you test your system for hallucinations
288. 9:41and consistency. And analytics tools
289. 9:43like Post Hog or Amplitude show you how
290. 9:45real people actually use it and where
291. 9:47they drop off. Governance is how you
292. 9:49keep it under control. And that mostly
293. 9:50comes down to cost because at scale AI
294. 9:52bills explode. And this is where you're
295. 9:55going to learn something called model
296. 9:57routing. For example, if I ask AI a
297. 9:59simple question like, "What color is the
298. 10:01Statue of Liberty?" I don't really need
299. 10:03the most advanced or expensive model to
300. 10:05answer that. Instead, I can save money
301. 10:07and use a cheaper model. But if I ask it
302. 10:09to break down complex tasks or write
303. 10:11essays for me, that may involve a more
304. 10:13costly expensive model. And knowing how
305. 10:15to separate which one at what time will
306. 10:17not only make you a great AI engineer,
307. 10:19but it'll take your project to the
308. 10:21finish line and save you lots of money.
309. 10:23Now, at this point you've developed
310. 10:24majority of the skills you need to
311. 10:26become an AI engineer. And as good as
312. 10:28you might be, no one will hire you until
313. 10:30you can turn your skills and knowledge
314. 10:32into actual proof. So, for level four,
315. 10:35our focus is now landing the offer. And
316. 10:38there are actually two things I want you
317. 10:40to focus on. The first thing you need to
318. 10:41do is build a real standout projects.
319. 10:44Far too many people just have basic
320. 10:46coding tutorial projects and wonder why
321. 10:48they can't get a callback from a
322. 10:49recruiter. You need to have real
323. 10:51impactful projects that show off your
324. 10:53skills. And lucky for you, there's this
325. 10:55free GitHub repository right here. Not
326. 10:57only does it cover everything you need
327. 10:58to know about AI engineering, but also
328. 11:01if you scroll down to the capstone
329. 11:02project section, you will find projects
330. 11:04like an autonomous multi-agent system
331. 11:06that plans and coordinates to finish a
332. 11:08complex task on its own. Or a large
333. 11:10language model built from scratch where
334. 11:12you actually learn agent orchestration,
335. 11:15transformers, and model optimization.
336. 11:17This will actually look so good on your
337. 11:19resume and show that you're actually
338. 11:20deep in the AI engineering field and not
339. 11:22just a buzzword spammer. The second
340. 11:24thing you need to do is get visible
341. 11:26because you could be the most talented
342. 11:28AI engineer in your city. But if no one
343. 11:30knows that you exist or what skills you
344. 11:33even have, no one is going to hire you.
345. 11:35So, build your projects in public. Post
346. 11:38what you're learning online, even if it
347. 11:40feels unfinished, because that trail is
348. 11:42what makes opportunities start coming to
349. 11:44you instead of you chasing them. One of
350. 11:46my friends would regularly post machine
351. 11:47learning content on LinkedIn. And by the
352. 11:49third time he made a post, he had
353. 11:51recruiters in his DMs offering to
354. 11:54interview him for a position. But,
355. 11:56beyond that, I would encourage you,
356. 11:57please go to meetups and conferences and
357. 12:00don't wait for people to notice you.
358. 12:01Instead, reach out first. Seek
359. 12:03connections. Seek referrals. Because in
360. 12:05a crowded market where there are lots
361. 12:07and lots of candidates, you need every
362. 12:08advantage going for you to land that
363. 12:10first job. Well, that's about all I have
364. 12:12in this video. I really hope that you
365. 12:13guys enjoyed it. And if you did, make
366. 12:14sure you hit the like button. Subscribe
367. 12:16if you haven't already. If you're
368. 12:17interested in my absolutely free tech
369. 12:18newsletter, link for that down below in
370. 12:20the description. And if you want to see
371. 12:22the interview that I had with the CEO of
372. 12:24Microsoft, Satya Nadella, you might want
373. 12:26to watch this video right here.