Forward Deployed AI Engineering · 13 weeks
A program for working professionals. You finish with four deployed systems, the client documents that went with them, and a capstone you have defended.
US postings for the role are already 4x all of 2025. Across 1,000 real FDE postings analysed, the five most common skills were Python, AI agents, TypeScript, AWS, and LLMs — the same stack this program is built around.
These employers hire for FDE roles. We are not affiliated with, endorsed by or partnered with any of them.
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A week in the life. Job descriptions for this role read like five different jobs stitched together, because the work really does change character from one day to the next.
You start the week on calls with the client's operations team, the business owner, and usually one doubtful senior executive. Nobody wants a pitch. You trace how their work actually flows, find the places where AI can move a real number, and rank the options by payoff against effort. By evening you have a one-page workflow map that you keep sharpening until Friday.
The client runs Salesforce, a home-built order system, a data warehouse, and an internal wiki that no one has tidied since 2019. Your job is to connect them: webhooks, queues, retries, authentication, idempotency. It is unglamorous plumbing, and it decides whether the AI feature ever touches real data.
Today you build: a RAG pipeline, tool calls, or an agent that must complete three steps in order without making things up. You write evals alongside it, because nothing ships to production unless you can measure whether it got worse.
You lead the steering committee and turn engineer-speak into plain business terms. "Retrieval recall fell four points after the chunking change" becomes "the assistant now answers correctly eight percent less often, and here is how we fix it." You also write the design doc the client's CTO will read late at night.
You release the system into a live environment with structured logs, tracing, metrics and a rollback plan, then watch how real users behave with it. Along the way you capture the moment the client's team starts to trust it. Those screenshots become the case study that wins the next deal.
That shift created a role — and the market is bidding for it faster than the talent exists.
Year-over-year growth in Forward Deployed Engineer job postings on Indeed, April 2025 to April 2026.
Of enterprise GenAI pilots showed no measurable P&L impact. The failure was traced to integration, not to the models.
Committed to forward-deployed engineering during 2026 by AWS ($1bn) and Microsoft ($2.5bn) alone.
Advertised salary range for forward-deployed engineers at US AI labs and enterprise software firms.
Your current profile covers part of it. Forward deployed engineering is where building, deploying and owning meet — and it is the part most engineers have never been asked to do.
You ship features against an internal roadmap. You will ship outcomes a client signs off on.
You keep systems running for internal teams. You will run them inside someone else’s constraints.
You scope the work and hand it over. You will scope it, build it, and defend it.
You design systems on paper. You will own one in production, in front of the client.
Begin by learning how the business really runs, not only what the brief says.
Turn the business's pain point into a well-defined problem that AI is genuinely suited to solve.
Choose what the AI will do, which data it relies on, and how you will judge success.
Build solutions that work with the client's existing data, systems, security rules and infrastructure.
Put working AI into live environments where real users rely on it.
Track how it performs, repair what breaks, and keep improving it from real-world usage.
Program Overview
Week 1 — The FDE Mindset
Week 2 — Vibe Coding
Week 3 — Consulting Only
Week 4 — GenAI Foundations
Week 5 — Rapid AI Prototyping
Week 6 — RAG & Semantic Search
Week 7 — AI Agents & Application Engineering
Week 8 — Project: Enterprise Knowledge Assistant
Week 9 — AI Evaluation
Week 10 — Deployment & LLMOps
Week 11 — Project: Production Readiness + Hardening
Week 12 — Solution Architecture & Consulting
Week 13 — Client Handoff & Capstone
Python
Git
TypeScript
Next.js
Vercel
Supabase
PostgreSQL
OpenRouter
LangChain
LangGraph
Pydantic
MCPPythonGitTypeScriptNext.jsVercelSupabasePostgreSQLOpenRouterLangChainLangGraphPydanticMCPOne structured cohort calendar, start to Anchor Client capstone — no open-ended pacing.
Complimentary with your enrolment, to strengthen prerequisites and readiness before week one.
Each shipped to a live URL, with its own trace and eval harness.
₹2,500 worth of tools during the cohort.
The real tools FDEs use daily, not slideware, not a single vendor's suite.
Structured checkpoints before you defend the final engagement live before a panel.
( Indicative ranges. Actual compensation varies by experience, company, and geography. These are general market ranges for these role types, not FDE School's own placement outcomes. )
Our intake is for software engineers, data scientists and AI practitioners with real production experience at scale, not for people just starting out. Admission is selective and batches stay small by design.
Admission is selective because people learn from the people around them. We look for those who lift the standard for themselves and for everyone in the room.
FDE isn't a role you can pick up from scattered tutorials or a short course. Companies such as Palantir, OpenAI and Anthropic want engineers who can take full ownership of AI systems in real production environments.
Getting into an emerging role early pays off out of proportion. Right now is that moment.
We know you might have some questions before getting started on our platform
An FDE is the engineer who makes AI work inside a real client's stack — not in a demo. They discover the actual problem, ship a deployed system, evaluate it honestly, and defend the engagement to a client, not just to a grader.
No — the Anchor Client is a single fictional client threaded through all 13 weeks, so your work compounds into one coherent engagement instead of disconnected weekly exercises.
We don't lean on borrowed badges or an alumni wall. The proof is the product instead: the syllabus is public, the price is stated upfront, and every gate requires a working deployed system, not a quiz score.
Freshers and working professionals from technical or analytical backgrounds — no minimum experience required, though a Bachelor's (CS/IT/Engineering/Maths or related) is expected. Yes, it's selective: apply online → pre-screening test → offer letter → pay and begin the bridge course.
₹44,999 + 18% GST (₹53,099 total). A ₹5,000 registration fee books your seat, and the 2-month bridge course is included free. EMI is available on major credit cards and through NBFC partners. We do not provide refunds. The registration fee of ₹5,000 is non-refundable in any case and can only be deferred to the next cohort.
4 deployed systems at live URLs (each with a trace and eval harness), a full client document set (discovery notes, PRD, estimate, priced proposal, SOW, risk register), one recorded engagement defence before a panel, and one written case study.
It ranges from about ₹20L at the entry level to ₹2.5Cr+ at the senior/staff end, with a typical Forward Deployed Engineer role landing around ₹45L–1.8Cr+. Actual pay varies by experience, company and geography.
The world is hiring and this School prepares you for it.