Before any code, you learn the seat itself: what a Forward Deployed Engineer actually owns, why most AI pilots stall before production, and why the role pays what it does.

• What separates an FDE from a senior dev or solutions architect, and why labs like Palantir, OpenAI & Anthropic are building out FDE teams
• Why AI projects don't fail in the demo; they fail after launch: integration, reliability & adoption gaps
• The FDE loop: Discovery → Prototype → Validate → Ship → Iterate
• The 2026 LLM landscape and where the FDE sits in it
• FDE traits: comfort with ambiguity, independent delivery, judgment on speed vs. quality

Every AI system is still just a program underneath. We lock in the engineering foundation before touching a single model.

• Python fundamentals, data structures & OOP for AI systems
• NumPy & Pandas for data handling
• APIs, JSON & system integration
• Logging, config management & modular, production-minded code
• AI-assisted development with Copilot & Cursor, writing and reviewing code faster

An FDE can't just prompt; they have to prove it works. You learn to talk to models well, then measure them with real eval harnesses a customer would trust.

• Transformers, attention, tokenization & context windows
• Prompt engineering: zero-shot, few-shot, chain-of-thought & role-based prompting
• System prompts, structured output & function calling with clean JSON
• Building eval harnesses so prompt changes are measured, not guessed
• Choosing the right model per use case: Claude, GPT, Gemini, Llama & Granite
• Cost, latency & reliability trade-offs you'll defend in front of a client

A demo RAG bot is easy. A RAG system grounded in a customer's messy, current data, one that holds up under real questions, is the FDE's bread and butter.

• Generating and comparing embeddings; chunking strategies & ingestion
• Vector databases: FAISS, ChromaDB & Pinecone
• Retrieval pipelines, semantic search & metadata filtering
• Hybrid retrieval, re-ranking & measurably reducing hallucinations
• RAG quality checks & grounding a customer would sign off on
• Agentic RAG: retrieval inside a reasoning loop

This is where you turn a corner, from calling a model to shipping something that reasons, acts and completes real work inside a client's stack.

• What makes a system "agentic" versus a simple chatbot
• Tool calling & chaining with LangChain Expression Language (LCEL)
• Extracting tool inputs from LLM outputs, then validating and executing the call
• ReAct style reasoning: think → act → observe
• Using built-in LangChain agents to analyze data, generate visualizations & run database queries
• Short-term & long-term memory for agents
• Designing and binding custom tools safely: accuracy, safety & cost tradeoffs

Single agents hit a ceiling fast. This module covers both how you give one agent structure and memory, and how you split real work across a team of agents the way you'd delegate across a team of people.

• Workflows built as graphs: state, nodes & edges, plus conditional logic & iteration
• Agents that improve themselves: Reflection, Reflexion & ReAct architectures
• Human-in-the-loop checkpoints for higher-stakes decisions
• Agentic RAG systems that route queries and support reasoning enhanced by retrieval
• Structuring agents, tasks & tools into crews; designing roles and delegating tasks
• Agents driven by conversation, built with CrewAI and AG2 (AutoGen)
• Agent-to-agent communication patterns & memory across collaborating agents
• Comparing frameworks (LangGraph vs. CrewAI vs. AG2) for real-world fit

MCP is quickly becoming the standard way agents connect to tools and data. This module doubles as direct prep for Anthropic's own architecture exam.

• Model Context Protocol: servers, clients, tools, resources & prompts
• Connecting models, tools and data through MCP
• Claude Agent SDK & Claude Code workflows
• Structured output design and context window management
• Reliability patterns: multi-instance review and coherence across long conversations
• Mapped to Anthropic's Claude Certified Architect – Foundations exam domains

This is the module that separates an FDE from every other kind of AI engineer, and almost no other course teaches it. You learn to walk into a client conversation and come out with a scoped, buildable plan.

• Running structured discovery sessions that surface the real problem, not the requested feature
• Jobs-to-be-done (JTBD) mapping and translating business requirements into technical scope
• Selecting the right architecture pattern (RAG vs. agents vs. classical ML) for a given problem
• Writing a graded discovery doc and solution architecture for a live mock client brief
• Security and procurement conversations: a simulated review and a technical security Q&A
• Communicating trade-offs and delivering bad news to a client without losing credibility

A working notebook isn't a deployment. This is where your system becomes something a customer can rely on: served, secured, monitored, and stress-tested on the cloud they actually run.

• Serving agents with FastAPI (streaming + auth) & containerizing with Docker
• Scaling with Kubernetes basics; deploying on Azure AI Foundry (with AWS as an alternate target)
• Grounding agents with Azure AI Search, responsible AI & content filtering
• Tracing & observability with OpenTelemetry; prompt versioning
• Evaluation pipelines with RAGAS, guardrails & hallucination checks
• Production economics: semantic caching, model routing & cost/latency optimization
• AI red-teaming: prompt injection, the OWASP LLM Top 10 & finding failure modes before the customer does

Everything comes together here: one panel-evaluated capstone that proves you can deliver, plus the interview and negotiation prep that turns that proof into an offer.

• POC sprints and the demo → production handoff done cleanly
• Capstone: deliver a deployed RAG + multi-agent service with a monitoring dashboard & 90-day adoption plan, evaluated by a mentor panel
• Interview prep: DSA refresh (sorting, recursion, trees, graphs, DP)
• The STAR+ behavioral framework & FDE-specific interview prompts; mock panels
• Sample practice papers for CCA-F and AI-103
• Resume, LinkedIn & offer-negotiation workshop built around your portfolio