Forward Deployed Engineer Program
A 10-Week Intensive to Become a Customer-Facing AI Engineer
A 10-week, build-first program for engineers moving into Forward Deployed Engineer roles. Ship an agentic MVP and a production deployment, then rehearse the FDE interview loop.
Programme details
- Level: Advanced
- Format: Hybrid — Live Sessions + Build Labs
- Duration: 10 weeks
- Fee: ₹1,77,000 (incl. GST)
- Next cohort starts: 24 October 2026
- Batch size: 20 learners
- Who it is for: Engineers targeting Forward Deployed Engineer, Solutions Engineer, or AI Implementation Engineer roles. Working Python knowledge required; no prior cloud or ML experience needed.
About this programme
Who This Is For
- Software engineers who want to move into customer-facing AI delivery roles
- Backend / full-stack engineers targeting Forward Deployed Engineer, Solutions Engineer, or AI Implementation Engineer roles
- ML/AI engineers who want the customer-scoping and enterprise-integration skills SWE and ML tracks don't teach
- Engineers preparing specifically for Palantir- or Anthropic-style FDE interview loops
Working knowledge of Python and basic programming fundamentals is required — this is not a first coding course.
Tools & Stack You Will Use
- Python, FastAPI, Docker
- AWS (IAM, Lambda, S3, Secrets Manager, Bedrock, AgentCore)
- Anthropic Claude API, Model Context Protocol (MCP), LangGraph
- Vector databases (pgvector), BM25 hybrid retrieval, Cohere Rerank
- RAGAS, Phoenix / Arize, LangSmith, Datadog / Grafana
- Guardrails.ai, NeMo Guardrails, Llama Guard
- Auth0 (SSO), Microsoft Presidio (PII redaction)
- vLLM on Modal (OSS deployment path)
How You Will Learn
- Weekly live sessions with a session deliverable due each week
- One continuous capstone engagement scenario carried across all 10 weeks
- Two shipped projects: an agentic MVP with MCP server, and a production-grade enterprise deployment
- Mock customer discovery calls, a mock client kickoff workshop, and recorded mock interviews
What Makes This Different
Most AI engineering courses stop at "build a working agent." This program covers the two things that actually separate an FDE from a backend engineer who can prompt: production discipline (evals, cost engineering, observability, guardrails) and the enterprise + commercial wiring (SSO/RBAC, ontology, compliance literacy, Statements of Work, pricing, and post-deployment ops) that make an AI system deployable and sellable inside a real enterprise.
Final Outcome
You leave with a complete, portfolio-ready FDE engagement package — problem brief, agent system, enterprise wiring, Statement of Work, handover doc, ops runbook, and a 5-minute Loom walkthrough — plus rehearsed decomposition-case and behavioral interview reps and a compensation negotiation script.
What you will learn
- Stand up a production-grade FastAPI + LLM service with structured logging, retries, and secrets handling
- Run customer discovery and translate ambiguous business problems into scoped technical engagements
- Design and ship multi-agent systems (Planner → Executor → Critic) with an MCP server
- Build production-quality hybrid retrieval (BM25 + vector + reranking) over real document corpora
- Build an evaluation suite (LLM-as-judge + golden sets) and engineer for token cost and latency
- Deploy an LLM agent to AWS Bedrock and an OSS target with CI/CD eval gates and guardrails
- Wire SSO, RBAC, audit logging, and PII redaction into a production agent using ontology-first design
- Write a Statement of Work, price an AI engagement, and run a structured incident RCA
- Pass a Palantir/Anthropic-style decomposition case interview and an FDE behavioral interview
What is included
- 10 weekly live sessions with a shipped deliverable each week
- Project 1: an agent system MVP (LLM + tools + memory + MCP server) scoped to your capstone scenario
- Project 2: a production-grade deployment with evals, observability, guardrails, and CI eval gates
- A complete capstone portfolio: problem brief, ontology, agent, enterprise wiring, SoW, handover doc, and ops runbook
- Two recorded mock decomposition case interviews and one STAR+ behavioral mock
- A written FDE compensation negotiation script
Prerequisites
- Working knowledge of Python (functions, classes, basic async)
- Comfortable with Git and the command line
- Basic familiarity with REST APIs
- No prior cloud or AI/ML experience required
Topics covered
Forward Deployed Engineer, FDE, AI Agents, MCP (Model Context Protocol), RAG (Retrieval-Augmented Generation), AI Evaluation, LLMOps, AWS Bedrock, Enterprise AI, Customer-Facing Engineering, Capstone Project, Interview Preparation
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