AI Forward Deployed Engineer — Foundation
Engineers who can find the use case, ship the agent and prove the ROI
An 80-hour B2B program that builds project-ready AI Forward Deployed Engineers and Architects — practitioners who can sit inside a client's business, find the use case worth doing, design and build the agent against real systems of record, harden it for production, and defend the numbers afterwards. Deep agentic engineering, AI-native software delivery, production architecture and the consulting muscle to convert a pilot into a funded implementation. Includes an 8-hour domain elective in Retail & E-commerce or FinTech & BFSI.
- Model- and cloud-agnostic — Claude, GPT, Gemini, Qwen & DeepSeek across Bedrock, Vertex, Foundry and on-premise
- 57% of the hours on hands-on agentic engineering — agents, MCP servers, multi-agent systems, evals and guardrails
- Ship autonomously with Claude Code, OpenAI Codex and spec-driven development
- 8-hour domain elective — Retail & E-commerce or FinTech & BFSI (12 domains available)
- Two client-grade capstones + 6 months of post-program AI coaching
Curriculum
80 hours · 10.5 days · instructor-led + hands-on labs — a structured path from fundamentals to production-ready skills
- What a Forward Deployed Engineer / Architect actually does
- AI, ML and deep learning — enough to reason about capability, cost and failure modes
- LLMs and multimodal LLMs: context windows, tokenization, reasoning modes, model families
- Demo: solving ML and NLP problems with LLMs — and when that is the wrong choice
- RAG pipelines: ingestion, chunking strategies, embeddings and vector stores
- Retrieval mechanics, hybrid search and reranking
- Accuracy engineering — query rewriting, chunk optimisation, measured improvement
- Advanced RAG: Agentic, Graph, Multimodal, Hybrid and Page Index
- Lab: full ingestion → indexing → retrieval → generation pipeline
- Agent design method: goal, boundaries, tools, memory, escalation, evaluation criteria
- Tool use, function calling and MCP client / host / server topology
- MCP server security — scoping, tool poisoning and confused-deputy risks
- Context engineering, agentic memory and deep agents for long-running tasks
- Multi-agent topologies and agentic pricing models
- Multi-agent systems with LangGraph, Google ADK and AWS Strands SDK
- Building a custom MCP server with FastMCP against real enterprise tools
- A2A protocol, reflection loops, Agent Skills and Agents.md
- Harness and loop engineering; ontologies, knowledge graphs and context graphs
- Model selection: capability, latency, cost, tool-calling reliability, residency
- Agent evaluation: golden sets, offline vs online, regression testing
- LLM-as-a-judge, Galileo AI and Arize Phoenix
- Agent security — identity, threat modelling, OWASP Top 10 for LLMs, red teaming
- Guardrails, observability, cost optimisation and post-production operations
- Governance: ISO/IEC 42001, NIST AI RMF, Databricks AI Governance
- Claude Code and OpenAI Codex — memory, tools, skills and the harness
- Spec-driven development with GitHub Spec Kit; BMAD
- MCP servers in the IDE; Agent Skills, Agents.md and plugins
- Agents across the SDLC — design, testing, DevOps, observability, incident management
- Build: Database Ops, FinOps and DevOps agents end to end
- Well-architected pillars applied to AI agents
- Agentic reference architectures and when to deviate from them
- Multi-agent communication: orchestration versus choreography
- The backend system design that decides whether the AI half works
- On-premise AI infra — NVIDIA, AMD and Apple Mac Studio / Mac Mini clusters
- Local development with Ollama and open-weight models
- Scaled inference with vLLM and CNCF llm-d on Kubernetes
- Deploying and scaling agents and MCP servers on AWS, GCP and Azure
- The consulting mindset and the four conversations an FDE must hold
- Use-case discovery: process mining, sizing the prize, scoring feasibility against value
- Structuring a pilot so it can graduate to production
- Post-production operations and an ROI model a CFO will accept
- Case studies: an agentic pilot, and an implementation for a bank
- Retail & E-commerce — catalog and OMS reality, fulfilment and returns economics, RTO risk
- FinTech & BFSI — payments, lending, credit risk, KYC/AML and the ledger
- Systems of record: entities, data models and integration surfaces, hands-on
- Where agents can actually attach — and the governance that gates deployment
- 12 domains available; we scope the right elective with you
- Capstone A: an agentic system on a real operational workflow, integrated via MCP
- Capstone B: a grounded knowledge system over a messy domain corpus
- Evals, guardrails, tracing and a per-transaction cost model
- Sponsor-grade demo, handover artefact and panel review
Proven in delivery
Already delivered to enterprise teams
Real programs, for real teams — across seniority levels. Named case studies and participant testimonials to follow.
Enterprise Legal team
A practical AI enablement program tailored to legal workflows — prompting, document work, and responsible use.
Enterprise IT Operations team
Delivered to a mixed-seniority cohort — participants from 7 to 20 years of experience, learning together.
Programs run for mixed-seniority teams — from mid-level engineers to 20-year veterans — so everyone learns and applies it together.
Pricing
Competitive, tailored to format, scope & team size.
Bring this program to your team
Delivered privately to your team — onsite or virtual, across the US, Europe and beyond. Pricing is competitive and tailored to format, scope and team size. Book a call and we'll scope a program for you.
Book a scoping callNo placement guarantee — we connect, you interview. See FAQ for details.
Frequently Asked Questions
Ready to bring this to your team?
AI Forward Deployed Engineer — Foundationis delivered privately — onsite or virtual — and tailored to your stack and goals. Book a scoping call and we'll design the program for your team.