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

Deliverable: A clean, reusable Python toolkit for AI systems PythonNumPyPandas GitHub Copilot

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

Claude APIOpenAI APIGemini APIHugging Face

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

Deliverable: Production-grade RAG assistant on real data RAGFAISSChromaDBLangChain

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

Deliverable: an agent that executes tasks with real tool access LangChainLCEL

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

Deliverable: Multi-step research agent + a collaborative multi-agent crew LangGraphCrewAIAutoGen (AG2)

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

Deliverable: MCP-based workflow automation system MCPClaude APIClaude Agent SDK

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

Deliverable: Deployed, monitored service + red-team findings report FastAPIDockerKubernetesAzure AI FoundryAWSLangSmith

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