The fullstack job you have today won't exist in three years. The role replacing it is Forward Deployed Engineer. If you can do the work.
24 weeks. 6 phases. Built from how OpenAI, Anthropic, Palantir, and Salesforce actually deploy engineers to clients. Eval-driven from day one. Capstone-graded. No fluff.
01 / The market is reshaping
You can feel it already.
The standup that used to be five engineers is now three. The intern pipeline got cut. Your manager keeps asking what you're doing with AI tooling, in a tone that isn't curiosity. The job market hasn't collapsed. It's reshaping. The question is which side of the reshape you're on.
Fullstack postings, YoY
Aggregate job-board data shows fullstack and frontend roles compressing while AI / ML engineering postings expand. The total isn't shrinking. It's relabelling.
AI engineer vs. fullstack postings
At top-tier AI companies, postings tagged "AI engineer" or "Forward Deployed Engineer" outnumber pure fullstack roles roughly three to one. That ratio was inverted in 2022.
What hiring managers want
The bar at OpenAI, Anthropic, Palantir, and Scale for FDE roles is set well above standard fullstack interviews. The premium screen is for skills you don't already have — evals, deployment, client mechanics.
The market isn't shrinking. It's reshaping. The engineers who reshape with it get paid more. The ones who don't, don't.
02 / The role replacing it
Forward Deployed Engineer is the job that absorbs everything fullstack used to be — and adds the work fullstack didn't cover.
Palantir invented the title. OpenAI productionized it. Anthropic, Scale, Salesforce, and every serious AI infrastructure company now hires for it under one name or another: Forward Deployed Engineer, Solutions Engineer, AI Engineer, Applied AI. The work is the same. Take a model that mostly works. Make it actually work, inside a real client's mess.
Greenfield is easy. Production AI inside a Fortune-500 codebase, with a CTO who wants weekly demos and a CISO who wants their concerns answered, is hard. Few engineers can do it. The engineers who can are the ones being hired.
What an FDE actually does
- Deploy AI systems inside a client's codebase, infra, and data — not in a sandbox.
- Own the eval loop: define what "working" means, measure it, defend the number.
- Translate fuzzy business asks into scoped engineering work, then push back when the ask doesn't survive contact with reality.
- Sit across the table from a CTO on Tuesday and write production Rust on Wednesday.
What the bar looks like
Hiring loops at the AI labs and applied-AI firms screen for evals you have shipped, RAG systems you have measured (not just built), and client-style discovery you can demonstrate on demand. The engineers who clear it have been doing this work for at least one year. This program is how you build that year.
Two paths
Pick the track that matches where you are now.
Same destination, two different starting points. Both end in a simulated engagement graded on the FDE rubric.
FDE Readiness — 24 weeks
The full program. Six phases, in order, designed for engineers ramping from production fullstack experience to client-deployment-ready FDE work. Weeks 1–4 build the codebase-reading foundation everything else depends on.
For engineers with strong fullstack experience but new to applied AI.
Sign in to enrollSenior FDE — 6 weeks
The compressed conversion for engineers who already ship serious production code. Skips the codebase-reading ramp and goes straight to LLM engineering, eval-driven development, production design, and a full simulated engagement.
For senior engineers who already build — assumes the foundation is there.
Sign in to enroll03 / The 24 weeks
Six phases. In this order. No reordering, no skipping.
The structure is the result of looking at how OpenAI, Anthropic, Palantir, and Salesforce actually onboard and deploy engineers. The two phases other programs cut are the two we defend hardest.
- 01Weeks 1 – 4
Production Codebases
Reading code you didn't write. Distributed systems vocabulary credible enough for Phase 2.
Capstone→ Time-to-first-patch in an unfamiliar OSS project - 02Weeks 5 – 8
LLM Engineering Core
RAG, agents, tool use, prompt engineering. Build the systems you'll later rip apart with evals.
Capstone→ Domain-specific RAG with hybrid retrieval - 03Weeks 9 – 12
Eval-Driven Development
Defend this phaseError analysis, LLM judges, eval CI. Measure first, build second. The phase that compounds.
The differentiator. Most courses skip this. Engineers who finish this phase well are ahead of 80% of the candidate pool.
Capstone→ Evals-first refactor of the Phase 2 RAG project - 04Weeks 13 – 16
Production System Design
Multi-tenant AI architecture, cost & latency budgets, failure modes you only see at scale.
Capstone→ 15 – 20 page architecture proposal for a real client scenario - 05Weeks 17 – 20
Client-Facing Skills
Defend this phaseDiscovery, scoping, stakeholder management, pushback. The skills that decide whether a deployment ships.
What most programs skip and what makes deployments fail. Code that works doesn't ship if no one trusts the engineer holding it.
Capstone→ Internal discovery → proposal → prototype → present - 06Weeks 21 – 24
Engagement Simulation
Synthesize everything under realistic client pressure. A simulated 4-week engagement, graded on a 5-axis rubric.
Capstone→ Full simulated engagement, manager review, written retro
04 / Why this works
Three things every other AI bootcamp gets wrong.
Eval-driven from day one
Most courses teach you to build. This program teaches you to measure. Phase 3 is dedicated to error analysis, LLM-as-judge, and eval pipelines — the same techniques Hamel Husain, Eugene Yan, and the Anthropic applied team use on real deployments.
Client-facing skills, not just code
Phase 5 is four weeks of discovery, scoping, stakeholder management, and how to push back without losing the engagement. Most engineers never get this training. Most failed deployments fail here.
Capstones graded on a 5-axis rubric
Every phase ends with a real deliverable. Manager-reviewed, scored on five axes, with a 3.5/5 weighted threshold to pass. The bar is the bar. "Submit and move on" isn't an option.
05 / Honest answers
Questions, answered without the marketing throat-clearing.
- Is this for junior developers?
- No. The program assumes you can ship a non-trivial fullstack feature on your own. If you can't read another engineer's code without hand-holding, finish that first. Phase 1 will eat you otherwise.
- Will this guarantee me an FDE job?
- No. No program can. What it can do is close the skills gap between "strong fullstack" and "hireable as an FDE at a serious AI company." Whether you get the offer is between you, the interview loop, and the market.
- How much time does this actually take?
- 8 – 12 hours a week, for 24 weeks. Some lessons are 90 minutes. The Phase 3 evals capstone is 15+ hours. We tell you the honest number on every lesson and capstone — round up, not down.
- What happens if I fail a capstone?
- You retake it. The 3.5/5 weighted threshold is the bar. Soft failure isn't a thing. The capstone is the proof that the phase actually changed how you work — without it, the phase isn't done.
- Why LinkedIn login? Why not email and password?
- Two reasons. We verify you're a real engineer with a real career, not a throwaway address. And the LinkedIn profile is what hiring managers check first — finishing this program shows up where it matters.
- What's the stack?
- The course platform: Next.js + Tailwind + SQLite, Dockerized. The course content: Python for evals and LLM work, TypeScript for production frontends, with deep dives into Postgres internals, RAG architecture, and applied prompting. Stack details inside Phase 1.
06 / The next move
24 weeks from now, you've either done the work or you haven't.
The role is real. The market is moving. The honest path is the one where you spend the next six months becoming the engineer that gets hired into it.
Profile data is used to verify enrollment. Nothing is posted to LinkedIn on your behalf.