Forward Deployed AI Engineering
Embed inside enterprise clients, scope ambiguous problems, ship production AI on their infrastructure
11 phases. 58 lessons. 58 labs. 4 projects. The role Palantir invented and OpenAI, Anthropic, and every AI startup is hiring for. You learn client discovery and problem decomposition, enterprise data engineering, production RAG on proprietary documents, agent orchestration in client environments, enterprise SSO and legacy integration, cloud deployment behind firewalls, AI evaluation and observability, production hardening, and the consulting and communication layer that separates FDEs from regul
- Lessons: —
- Labs: —
- Projects: —
- Level: Beginner
Curriculum
- The FDE Role & Deployment Mindset — What an FDE is, Palantir origin story, T-shaped profile, Dev vs Delta boundary, five deployment phases, radical ownership, AI last mile problem
- Client Discovery & Problem Decomposition — Discovery methodology, 5-step decomposition framework, scoping conversations, Minto Pyramid Principle, turning vague problems into specs, managing scope creep, discovery deliverables
- Enterprise Data Engineering — Schema recovery, advanced SQL for client databases, ETL/ELT pipeline design, dbt data quality, document processing for RAG, Medallion Architecture, Spark for distributed processing
- Enterprise RAG Systems — RAG pipeline end-to-end, vector database selection, hybrid search with RRF, retrieval tuning on proprietary documents, document-level access control, RAG evaluation, debugging retrieval quality
- Agent Orchestration in Client Environments — LangGraph stateful agents, multi-agent delegation, human-in-the-loop gates, tool integration with enterprise systems, agent evaluation in production, agent deployment on client infrastructure
- Enterprise Integration & Authentication — SAML 2.0, OIDC, OAuth 2.0, SCIM provisioning, bridging auth protocols, legacy system integration, enterprise platform integration (Salesforce, SAP, ServiceNow)
- Cloud Deployment in Client Environments — VPC configuration, private Kubernetes, VPC Service Controls, deploying behind client firewalls, Terraform for reproducible deployments, Docker and Kubernetes for AI workloads
- AI Evaluation & Observability — Evaluation framework design, LLM-as-a-Judge, AutoSxS win-rate analytics, LangSmith/Braintrust/HoneyHive/Phoenix, production monitoring, drift detection, CI/CD for AI deployments, guardrail regression testing
- Production Hardening & Operations — PoC-to-production gap, performance optimization, reliability engineering, incident response for AI, runbooks and handovers, deployment patterns (Bootcamp, phased rollout, canary releases)
- Client Communication & Career — Stakeholder management, executive communication, consulting skills, client simulation practice, portfolio building, FDE interview prep, 6-week preparation plan
- Capstone — Enterprise Deployment Project — End-to-end enterprise RAG + agent build, production hardening & CI/CD eval gates, client presentation with the Minto Pyramid Principle, FDE portfolio finalization
Skills You Will Learn
- Problem Decomposition
- Enterprise RAG
- Agent Deployment
- Enterprise Integration
- AI Evaluation
- Production Hardening
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