Forward Deployed Engineer – Foundation [CFDE-F]

What the role is and why it exists. How FDEs differ from solutions engineers, consultants, and product engineers. The deployment lifecycle: post-sale handoff, discovery, scoping, build, deploy, iterate, hand-back. How FDEs are measured: deployment velocity, customer success, product feedback quality. Career paths: FDE to Senior FDE to Head of Customer Engineering to founder.

Running discovery sessions with enterprise and SME clients. Identifying the real blocker when the client cannot articulate it. Asking the right questions before writing a single line of code. Translating vague business pain into a scoped, buildable technical problem. Stakeholder mapping.

Translating scoped problems into technical requirements. Writing architecture briefs that a client’s CTO or technical lead can review and approve. System design documents. Decision logs that make reasoning visible. Making work handoff-ready.

You never start from scratch. How to land in an unfamiliar codebase, understand it quickly, identify integration points, and work within constraints you did not design. Reading undocumented APIs. Working with tribal knowledge. Building on top of messy reality. Ad-hoc exploration using Python, Pandas, SQL, Jupyter to understand the client’s data reality.

REST and GraphQL API design and consumption. Connecting systems that were not built to talk to each other. SQL and NoSQL database pragmatism: choosing and justifying the right tool. Vector databases. Production-grade data engineering: building reliable, scalable pipelines that feed AI systems. Data ingestion, transformation, Spark, and Airflow.

Working across OpenAI, Claude, Gemini, Mistral, and open-source models (Llama, DeepSeek, Qwen). Practical differences in API design, pricing, latency, capability, and compliance posture. Building a model selection framework: when to recommend which provider to a client and justifying the choice on cost, performance, and regulatory grounds. Emphasis on swap-ready architecture over provider lock-in, since the frontier moves fast and named examples date quickly.

System prompts, structured outputs, chain-of-thought, few-shot learning, guardrails. The difference between a demo prompt and a production prompt: reliability, edge cases, cost, latency. Building prompt systems that hold up when real users do unexpected things. Prompt versioning and regression testing.

Document ingestion, chunking strategies, embedding models, vector databases (Pinecone, Weaviate, ChromaDB), retrieval, hybrid search, re-ranking, generation. Building a complete RAG pipeline against real-world data. The single most common FDE build task in 2026.

Building evaluation suites for AI systems: faithfulness, relevancy, hallucination rate, regression detection, bias, grounding gaps. Automated evaluation vs human evaluation. Evaluation metrics a client can understand and run themselves.

Agent frameworks: LangGraph, CrewAI, OpenAI Agents SDK, MCP, tool orchestration. The agent loop underlying all of them: plan, act, observe, repeat. Single-agent and multi-agent system design. When to use agents vs when a simpler pipeline is better. Multi-agent orchestration, human-in-the-loop patterns, and agent failure modes at the Foundation level.

CI/CD pipelines for AI applications. Monitoring, observability, alerting. Cost optimisation: token budgeting, caching, model routing. Deploying on Vercel, Railway, AWS Lambda, or lightweight cloud infrastructure. Model versioning and experiment tracking. Environment management with Docker and cloud CLIs. Scoped to what an SME FDE actually does.

When to fine-tune vs prompt-engineer. Making the business case to a client. LoRA/QLoRA. Training data curation and quality. Evaluation gates before deploying a fine-tuned model. Version control for models. Cost implications and tradeoffs.

Getting from working demo to live production. The 80% of work that happens after the demo works. Onboarding client teams to use the deployed system. Reducing resistance by tailoring to real workflows rather than forcing workflow changes. Measuring adoption. Defining success metrics with the client. Handoff documentation.

Explaining technical architecture to non-technical people. Running effective client meetings. Status updates that build trust. Managing scope creep and setting expectations. Saying no constructively. Written communication: implementation guides, decision logs, handover documentation.

The FDE’s core thinking skill. Taking a massive, ambiguous problem and breaking it into shippable chunks. Prioritisation under time pressure without sacrificing production quality. Making and documenting assumptions. Shipping MVPs that are genuinely viable. Breaking work into 1-week milestones. Delivering a walking skeleton early. Problem decomposition and MVP prioritisation.

Realistic engagement simulation running over three weeks. You receive a business brief from a mock client with ambiguous requirements, a messy dataset, and a tight deadline. Deliverables: discovery notes, architecture document, working deployed prototype, client-facing documentation, and a written record of the design decisions you made and why. Assessed by AIU practitioner reviewers against a published rubric covering whether the solution works, whether the code is handoff-ready, whether you solved the right problem, and the quality of your written technical communication.