A 13-week program for engineers who already ship code. You get a 1:1 roadmap call in your first week, build eight production AI systems that demonstrate current, in-demand AI engineering skills like agentic RAG, multi-agent systems and evals, and work with a placement team on job outreach, applications and interviews.
Hear from a senior platform engineer at Meta, a DevOps engineer, and a 15-year senior software engineer.
One of the most transformative things that I've done.
...It's very structured and in-depth, and whenever you have some questions there are always folks available to answer them...
Now I know how to make it do what I want it to do.
Before the program I was using AI with just prompts and as an elaborate search engine...
...I'm still just scratching the surface...
The information here goes beyond any other program.
I had no AI experience, Machine Learning, anything like that...
I've got a partial sponsorship from my employer to build a project that could save our company millions of dollars...
Two were hired onto GenAI teams at Capital One and L'Oréal. Two were promoted to AI lead at the companies they already worked for. Pick one to see how they got there.
Anup V.Cohort 1Backend software engineer, between jobs.
Strong engineering foundation, no production AI experience, stuck in the tutorial learning cycle.
Completed all eight production builds.
Hired onto the Generative AI team at Capital One.
DJCohort 1DJ · AI leadSoftware engineer, employed, seeking an AI title and a compensation increase without changing companies.
No production AI systems in his portfolio, and no internal AI project scoped or approved.
Identified an internal use case, built a production AI system with technical guidance and prepared the promotion case, all while working full time.
Promoted to AI lead, managing a team of three engineers, at the same company.
Ruby J.Cohort 1Principal engineer, trajectory had plateaued.
Senior engineering credibility, no AI-specific production work.
Built an AI pipeline for her HR team that scored thousands of resumes against hundreds of thousands of job descriptions.
Promoted into a new AI lead role her company created for her.
Julia P.Cohort 2Frontend engineer, no Python experience, no AI background.
Significant: no starting foundation in the primary language or domain.
A custom roadmap built from her actual starting point. She did not skip the foundation steps.
Hired onto the GenAI team at L'Oréal within 3 months of program start.
Anup's story, 1 of 4
Your 1:1 roadmap call happens within 7 days of enrolling.
Getting you hired or promoted into an AI engineering role.
A 1:1 roadmap call with Zao and Dipen. You map your current stack against the AI roles you want, and the 60 to 70 hour curriculum gets cut down to the 20 to 30 hours you actually need.
Eight production systems, including retrieval pipelines, a fine-tuned model and multi-agent applications. Each carries evaluations and a failure analysis, and a senior engineer reviews it before it goes into your portfolio.
The placement team rebuilds your resume, LinkedIn and portfolio site, contacts hiring managers, applies to openings for you, and coaches you through interviews and the offer.
Seven parts, each doing its share of getting you into an AI engineering role. Pick one to see it up close. Most are real screens from the course platform, live coaching, a student's build and the cohort community.
Before joining the AI accelerator, you feel everything could be an API call to the GPT API or Gemini API, and everything feels rosy.
Going from a project to a product, that's the transformation the AI accelerator helped me identify.
There was no way to know if I'm making progress after changing something in the prompt, because I have no evals at all.
They were very emphatic: you need to create your own evals that will keep you north and give you direction whether you are making progress or not.
I was looking to learn more about AI that wasn't just piecing together random things from the internet. I wanted a more structured approach.
What I'm already doing now is just building. I want to build some projects and some products with this knowledge.
I was barely keeping up with some research papers before.
Now I feel like I can understand even the things that are popping up every day, even reading the model cards, and lose the fear of trying any model or any technique.
Using AI tools at work and building the systems behind them are different skills. The postings and salaries below are for the second one.
You use Cursor or Claude Code every day. You have finished courses, wired up an API key, maybe shipped a feature. Then an interviewer asks how you would evaluate retrieval quality on a legal corpus, and you do not have an answer. You know what RAG and fine-tuning are, but you have not combined them in a system and measured it.
From 2024 to 2025, ML and AI engineering postings grew about 40%, frontend postings fell about 10%, and backend grew about 4% (Bloomberry). LinkedIn counts roughly 1.6 million open AI roles against 500,000 qualified people.
Vertical AI roles need someone who can ship production software and add AI to it. You already do the first part. DJ and Ruby, above, moved from senior and principal engineer into AI lead roles at their own companies.
I'm overqualified and underqualified at the same time.
How do I get past the filter of three years deploying ML models in production?
I have been on Udemy for a year and have nothing to show for it.
Everyone on my team is talking about AI. I nod along.
I can see myself easily becoming deprecated over time.
I wouldn't call myself an AI engineer. I'm more of an AI consumer at this point.
You sign up for an AI newsletter. Every day there is something coming to the mailbox and you feel like this is going so fast. I don't understand half of what they're saying.
If you ignore AI and how it can boost your productivity as a software developer, you'll get left behind.
For example, a law firm hires an AI engineer. That engineer takes 20 years of past case files, wins, losses and settlements, and builds a system that analyzes incoming cases and recommends whether to accept them, instead of junior lawyers spending 40 hours per case. The system connects to the firm's email, billing and CRM. The job needs your software engineering background plus the retrieval, fine-tuning and evaluation skills the program teaches. It does not need a PhD.
I am not going to go with a PhD to get that job. It's not my interest.
It was more about high-level things, how LLMs work behind the scenes, but nothing related to the real job industry.
You finish with the Evidence Pack: your reviewed builds, an open-source PR, two blog posts, and a rebuilt resume and portfolio.
You have a 1:1 roadmap call with Zao and Dipen in your first week. Together you map your current stack against the AI roles you want, pick a domain track, and cut the 60 to 70 hour curriculum down to the 20 to 30 hours you need. Then you work through the foundation units: AI product foundations, AI tools, and prompt engineering.
A week-by-week roadmap aimed at named roles and comp ranges, a working dev environment, and a list of builds matched to what postings for those roles ask for.
"Zao pushed me into starting the project, and he said the homeworks are really designed as a template from which you can copy and paste code to begin doing your project."
SunjithwatchCo-founder · AdmoreBoth tracks share the 16-unit core curriculum. The Consultant Accelerator adds Bootcamp 2: three advanced units and 25 lessons on state-of-the-art RAG, evals and RLHF.
AI EngineerThe core curriculum as is · 6 domain builds · 5 open-source targets
Cohere Rerank
Sentence Transformers
ColBERT
ColQwen-Omni
BERTopicBraintrust
Logfire
DSPy
OpenPipe ART
PufferLibThere are eight required production builds and an optional ninth. Each ships with a goal statement, an architecture diagram, evaluations with metrics, an iteration log, a README a reviewer can run, and a 90 to 180 second demo. A senior engineer reviews every one in live coaching before it counts.
"Everybody would give you a to-do app: use PyTorch, use a bunch of LLMs and stuff."
Sachin PanemangalorewatchSenior engineer · MetaA browser extension captures LinkedIn postings, FastAPI ingests the HTML, an LLM agent extracts position, company, salary and description, embeddings land in ChromaDB, and a Streamlit chat queries the corpus with RAG. Airflow runs the pipeline on a schedule.
Pick one of 20 domain tracks. Your builds and blog posts focus on that domain.
Select a track to see how it changes the syllabus above.
Phase 3 starts in week 9. From then on, the team runs outreach and applications for you each week.
Sources hiring managers and recruiters at companies with live AI roles and runs direct outreach with your Evidence Pack attached.
Owns your search plan, tracks every conversation, and checks in with you every week through the full placement process.
Applies to active AI openings for you every week, once the team has cleared your builds and resume for placement.
Your point person for schedule, blockers and accountability from onboarding through graduation.
2026 is the first year we run the full placement team. Earlier cohorts were skill-focused, and most of those students were internal builders or managers rather than active job seekers. Zao and Dipen are still personally involved with every student right now. That will not be true once the program scales.
They scope an AI project at work, build it in the program, and use it to move into an AI role there.
It's a game changer.
I was barely keeping up with some research papers before, and now I feel like I can understand even the things that are popping up every day, and lose the fear of trying any model or any technique.
It was something that I could do.
I thought it'd be totally overwhelming, but it really wasn't that bad.
I can develop any kind of AI applications.
I'm confident that I can develop any kind of AI applications, including AI agents or MCP servers.
Prices are 2026 figures from public sources.
A master's is the right choice if you want to publish research. The lower-cost programs (Georgia Tech OMSCS, UT Austin MSDS) teach theory that runs 12 to 18 months behind production, and none of them guarantee placement.
You learn how the algorithms work. Deploying and monitoring them is rarely on the syllabus.
Andrej Karpathy's course is an excellent starting point. YouTube and Udemy cover the material. However, the line we hear most often on calls is: "I have learned a ton, and I still cannot land a role."
Nobody reviews your code, runs mock interviews with you, or sends your work to hiring managers.
Programs like Interview Kickstart drill LeetCode for generalist roles and only publish pricing on a sales call. AI engineering interviews are project-based.
You practice interviews but have no AI projects to talk about in them.
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04You get a 1:1 roadmap, eight builds reviewed by a senior engineer, a placement team, and a conditional tuition-back guarantee. We only admit engineers who already ship code.
Things that I have been taught here were never taught at my school, and specifically in my master's program I didn't find those topics.
You look up YouTube and there's hundreds of contents there and you don't know where to start. I was very intimidated.
I took another program I won't mention. It was more of an academic setting. It was a good program, but it didn't go into depth.
I knew AI was the future. I wanted to get in on it. That's why I decided to do the boot camp, and it's turned out really well.
Some of them are partly true, and the answers say which part.
It is. Separate the two questions first: is it cash flow, or something about the program? Cash flow has two answers: one-time pay in full or three monthly payments, with financing options available. For the program question, here is the math. The average US AI engineer earns about $206,000 against a $134,000 software engineer average, so at that delta tuition is about six and a half weeks of the raise. There is no income share and no pay-after-placement. If the only way you can do this is to pay after you are hired, this is not the right program.
It is about an hour on weekdays plus two protected blocks on the weekend, and everything live is recorded. Most of the time engineers lose to AI is spent deciding what to learn next, evaluating another framework, or restarting a path they abandoned. The Phase 1 roadmap cuts that time by skipping units you already know.
"It was definitely a lot, don't get me wrong, but it was also manageable if you can just follow the path of the course."
James NewmanwatchTech leadYou are describing a horizontal AI engineer, roughly 5,000 jobs globally. We train vertical AI engineers, where the requirement is strong software engineering plus AI skills and there are about 1.6 million open roles. The program is code-first. You will need matrix multiplication and three or four other concepts, and we teach them at the moment you need them.
"I've been trying to jump on the Gen AI bandwagon since the revolution started, but I had limited knowledge in Python and machine learning."
SunjithwatchCo-founder · Admore2026 is the first year with the full placement team, so we do not quote a placement rate. What we can show you is the process: an Evidence Pack hiring managers can open, three internal interviews before your first real one, a team running outreach and applications for you, and a tuition-back guarantee if you do the work and it does not land.
In the program you build a five-agent system around a real business goal, set up production evaluation and monitoring, and trace a failing retrieval pipeline to its root cause. Your engineering experience is the part we do not have to teach. We add the AI work on top of it.
Yes, and the material is good. What free material lacks is an order to learn it in, feedback on your work, and a way to get that work in front of employers. A video cannot review your pull request, run three interviews with you, or send your portfolio to a hiring manager.
Python is the shared language we teach in because it is the industry standard. Most engineers pick up the syntax in the first two weeks. If you have never used it and do not want to learn it, this is not the right fit.
The cohort skews senior, usually 15 to 25 years of experience. The engineers you compete with for AI roles mostly transitioned recently, so nobody has ten years of production AI. Your domain experience helps, because you know what the system needs to do before you build it.
"It's an incredible journey for me being later in my career, where I've been away from engineering for a long time."
Michael von BodungenwatchCXO · e-commerceMost AI bootcamps teach basic AI programming and API wrappers. We teach full model adaptation: retrieval optimization, fine-tuning with LoRA and DPO, multi-agent systems, and evaluation. Ask any program three questions. Is there an active placement team, or only career coaching? Do they teach evaluation-centric production AI, or chat demos? Can the instructors show you production systems they built?
"I feel like it's more than a boot camp. It's more like an accelerator."
Sachin PanemangalorewatchSenior engineer · MetaYou do not need to explain a gap. Your GitHub carries production builds with evaluations, an accepted open-source PR, and technical writing in your specialization. The resume points at the proof, and the interviews are project-based rather than LeetCode.
Dipen has published 16 research papers and has reviewed 100+ for journals. Zao runs newline and ships its AI products.
I started learning deep learning in 2016 and 2017 from tutorials, online courses and textbooks. I would get stuck on one error for two, three, four weeks because I had nobody to ask. Almost nothing was end to end.
The engineers who call us now describe the same thing with better tools. They have learned a lot. They still cannot land the role, because they have nothing a hiring manager can review.
So we built the program I wanted back then: a roadmap that starts from what you already know, production builds reviewed by engineers who ship AI systems, and a placement team that sends your work to hiring managers. We use everything we teach on our own codebase every day.
Most students enroll to change jobs. About 30% stay at their company and move into an AI role there. Others build a product or practice of their own.
Move into an AI Engineer, Applied AI or AI Platform role at a product company. You get the roadmap, the Evidence Pack, and the placement team until you have an offer.
Stay at your company and move into an AI role there. You scope an AI project at work, build it in the program with senior review, and bring the results to your manager. In several cases, the company paid part or all of tuition.
Ship an AI product solo or with a small team, or build internal systems that replace SaaS tools. The curriculum is the same, and your builds are chosen around your product.
I don't think I could have had those conversations without going through the course and doing the work.
Now I'm confident that I can use this knowledge that I have learned in boot camp in the real life projects that I need to drive in the corporate world.
I decided to build a product that came to my mind after going through the program.
At that difference, the raise covers tuition in about 6.5 weeks, before taxes.
Pick a track to see what its tuition covers.
you skip what you already know
The full Accelerator curriculum, plus:
where RAG, fine-tuning and RLHF fit in production
The full Consultant curriculum, plus:
mentored 1:1 by a published AI researcher
all of it on your own GitHub
All eight Accelerator builds, plus:
all of it on your own GitHub
Every Consultant build, plus:
results anyone can check
backed by the tuition-back guarantee
backed by the tuition-back guarantee
The Accelerator placement team, plus:
backed by the tuition-back guarantee
All three tracks share the core curriculum and the placement team, and each includes everything in the one before it. The consultant track adds Bootcamp 2: advanced agents, evals and RLHF. The researcher track adds research work and targets applied research roles. Not sure which fits? We recommend one on a free career strategy call.
It's well worth the money.
It's also a huge time savings that to me translates into just making a better decision up front.
| Master's in CS or data science | $7K to $80K+ | Two years. No placement guarantee. |
| Interview-prep bootcamps | $2,400 to $12,000 | Pricing only on a sales call. |
| Coding bootcamp with a job guarantee | about $11,000 | Strict eligibility. Refunds rarely claimed. |
| AI Engineering Accelerator | $8,900 | 13 weeks. Placement team. Tuition-back guarantee. |
Prices are 2026 snapshots from public sources. Read the eligibility terms of any guarantee, including ours, before you sign.
"That completely changed. There are so many things I want to build, and I got clarity and a path."
If you are not ready to enroll, watch the walkthrough first or reply to any email from Zao with your questions.
That's something the AI cohort provided to me: to know what to use in the real world, in the industry.
I quit my job because I thought I could actually build something and go somewhere.
It's like when I was in graduate school, but it's faster, and I'm learning things that I feel are useful and I can use now.
AI Engineering Accelerator$8,900 or 3 payments of $3,30013 weekstuition-back guarantee
See pricing
