Forward Deployed Engineer (FDE)
The engineer who ships AI inside the customer's world.
Forward Deployed Engineers sit between a product team and its largest customers — scoping the real problem, building the integration, and making an AI system work against messy production data. It is one of the fastest-growing and highest-leverage roles in AI companies today, and almost nobody trains for it. This programme does.
- Duration
- 16 weeks
- Weekly effort
- 10–12 hrs/week
- Level
- Advanced
- Format
- Live cohort + 1:1 mentorship
- Mode
- Classroom in Marathahalli & Live online
- Typical range
- ₹18–45 LPA
Outcomes
What you will be able to do
Not topics covered — capabilities you can demonstrate in an interview and on day one of the job.
- Run a discovery call and convert a vague business ask into a scoped, testable AI solution
- Build production integrations against unfamiliar customer systems and dirty data
- Design evaluation harnesses that prove an AI feature actually works for the customer
- Deploy into restricted environments — VPC, on-prem, air-gapped — and debug them live
- Write the solution design docs, runbooks and handover material a real deployment needs
Curriculum
6 modules over 16 weeks
Every module ends in working code that is reviewed by a mentor before you move on.
01The FDE Operating Model
Weeks 1–2
The FDE Operating Model
Weeks 1–2
- What an FDE actually owns, and where the role sits between product, sales and engineering
- Discovery: running technical scoping calls and asking the question behind the question
- Turning a business objective into a success metric you can measure in week one
- Writing a solution design document that a customer VP and a staff engineer both accept
02Integration Engineering
Weeks 3–5
Integration Engineering
Weeks 3–5
- Reading and mapping an unfamiliar customer schema under time pressure
- Building resilient connectors: REST, GraphQL, webhooks, SFTP, message queues
- Auth in the enterprise — OAuth2, SAML, SCIM, service accounts, rotating secrets
- Idempotency, retries, backpressure and the failure modes that page you at 2 AM
03Applied LLM Systems
Weeks 6–8
Applied LLM Systems
Weeks 6–8
- Retrieval over customer data: chunking strategy, hybrid search, reranking, citations
- Tool-calling and Model Context Protocol servers over customer systems
- Structured output, schema enforcement and graceful degradation
- Cost and latency engineering: caching, routing, batching, model selection
04Evaluation & Trust
Weeks 9–10
Evaluation & Trust
Weeks 9–10
- Building a golden dataset with the customer's own subject-matter experts
- Offline evals, LLM-as-judge, and knowing when the judge is wrong
- Online measurement: shadow traffic, A/B rollout, human review queues
- Guardrails, PII handling, red-teaming and the security questionnaire
05Deploying Into Someone Else's Estate
Weeks 11–13
Deploying Into Someone Else's Estate
Weeks 11–13
- VPC, on-prem and air-gapped deployments — what breaks and how to plan for it
- Infrastructure as code for repeatable customer environments
- Observability you can hand over: logging, tracing, dashboards, alerting
- Runbooks, SLAs, escalation paths and the production support handover
06The Customer-Facing Craft
Weeks 14–16
The Customer-Facing Craft
Weeks 14–16
- Demoing to executives without dumbing it down
- Handling scope creep, blockers and the difficult status update
- Feeding the field back into the product roadmap
- Capstone build, live defence, and FDE interview simulation
Capstone
What you walk out with
Portfolio work substantial enough to carry a technical interview on its own.
Take a simulated enterprise brief from discovery call to deployed, evaluated AI system with full handover documentation
Debug a deliberately broken customer deployment live, under time pressure, and write the incident report
Tools & stack
Roles this leads to
- Forward Deployed Engineer
- AI Solutions Engineer
- Solutions Architect (AI)
- Technical Account Engineer
- Deployment Engineer
Who it is for
- Software engineers with 2+ years who enjoy customer-facing work
- Solution architects and pre-sales engineers moving into AI
- Data and ML engineers who want scope beyond the notebook
Prerequisites
- Comfortable writing Python and reading someone else's codebase
- Basic SQL and REST API experience
- Willingness to present and defend technical decisions
Enrol
Get the full syllabus for Forward Deployed Engineer
Request the detailed module-by-module syllabus, current batch dates and fee structure. A counsellor will also check whether this is the right programme for your background.
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