Course · Forward Deployed AI Engineer
Build in the customer's world, not in yours.
A 6-week course for forward deployed AI engineers: scoping in the field, prototyping against messy customer data, integration, demos that convince, and handover that lasts.
- 6 weeks
- Advanced
- Enterprise
- Customer facing
- Delivery
- Duration
- 6 weeksDuration
- Effort
- 8 hours per weekEffort
- Level
- Intermediate to AdvancedLevel
- Modules
- 6Modules
The premise
The forward deployed engineer is the highest-leverage role in applied AI right now, and almost nobody trains for it. It is part engineer, part consultant: you sit inside a customer's constraints, find the problem worth solving, build it against data that is nothing like the demo set, and hand over something their team can keep running. This course teaches the engineering and the judgement together.
Who it is for
- Solutions and implementation engineers moving into AI
- AI engineers taking on customer-facing delivery
- Consultants and technical pre-sales in AI products
- Startup engineers doing bespoke customer deployments
Prerequisites
- Working knowledge of LLM applications and RAG
- Have integrated with a third-party API or database
- Comfortable presenting technical work to non-engineers
Outcomes
What you can do at the end.
Run a discovery session that surfaces the real problem, not the requested feature
Scope work you can deliver in a two-week window and defend the cut
Build against messy, permissioned, incomplete enterprise data
Integrate with systems you do not control and cannot restart
Demo in a way that survives hostile questions
Hand over with documentation, evals, and runbooks the customer's team can use
Syllabus
6 modules, in this order.
The role and its economics
4 hours
- What forward deployed engineering is, and what it is not
- Where value is created and where projects die
- Working across sales, product, and the customer's engineers
Discovery and scoping
6 hours
- Running a technical discovery session
- Separating the stated request from the underlying problem
- Feasibility triage: prompt, retrieve, fine-tune, or refuse
- Writing a scope that fits two weeks
Prototyping against real data
8 hours
- Messy documents, partial exports, inconsistent schemas
- Permissions, PII, and what you are allowed to see
- Building a vertical slice in days, not weeks
Integration in someone else's stack
8 hours
- Auth, networks, and enterprise constraints
- Working inside VPCs, proxies, and change windows
- Deploying where you have limited access
Evaluation the customer trusts
6 hours
- Building an eval set from the customer's own examples
- Agreeing acceptance criteria before you build
- Reporting quality in terms the business recognises
Demos, handover, and expansion
6 hours
- Demos that survive hostile questions
- Runbooks, documentation, and knowledge transfer
- Support boundaries and the second engagement
Capstone
Take a simulated enterprise engagement end to end: a discovery transcript and a messy data dump in, a scoped vertical slice, an agreed eval set, a working integration, a recorded demo, and a handover pack out.
Tools and stack
- Python
- FastAPI
- LangGraph
- Docker
- Postgres
- Cloud IAM basics
- Evaluation harnesses
- Runbook templates
Delivery
How it can run.
Live cohort
Corporate batch
Corporate batches run against your own stack and swap the capstone for an internal use case. Campus delivery includes syllabus mapping, rubrics, and question banks — and faculty training if you want to own it afterwards.
FAQ
Forward Deployed AI Engineer — questions
What is a forward deployed AI engineer?
An engineer who works inside the customer's environment rather than on a product team — scoping, prototyping, integrating, and handing over AI systems against that customer's real data and constraints. The title comes from Palantir; the pattern is now standard at AI companies doing enterprise delivery.
How is this different from a solutions architect role?
A solutions architect mostly designs and advises. A forward deployed engineer writes the code that ships, in the customer's stack, often within days. The judgement overlaps; the deliverable does not.
Is this course technical or consultative?
Both, deliberately. Roughly two-thirds is engineering against realistic mess, one-third is discovery, scoping, demo, and handover. Teaching either half alone produces the two common failure modes of the role.
Do I need enterprise experience to take it?
No, and that is often the point — engineers from product backgrounds take it precisely because they have never worked inside someone else's constraints. You do need solid LLM application experience.
What does the capstone involve?
A simulated engagement with a discovery transcript, a deliberately messy data dump, and a fixed two-week scope. You deliver a working slice, an eval set agreed against acceptance criteria, a recorded demo, and a handover pack — reviewed the way a customer would review it.
Can this be run for a delivery team?
Yes, and it works best that way. Corporate batches replace the simulated engagement with a live or recent one of yours, so the team leaves with a scoping and handover standard they actually use.
Other courses
12 weeks
Full Stack AI Engineering
A 12-week full stack AI course: build, deploy, and operate an AI application end to end — API, retrieval, agents, frontend, infrastructure, and monitoring.
8 weeks
Generative AI
An 8-week generative AI course covering LLMs, embeddings, RAG, fine-tuning, multimodal models, and safety — with working code at every stage.
8 weeks
Agentic AI
An 8-week agentic AI course on tool use, reasoning loops, memory, multi-agent coordination, MCP, and the evaluation that keeps autonomous systems trustworthy.
Run this
for your people.
Tell us the cohort size, the hours available, and whether this is a corporate batch or a campus program. Scope and price come back in writing within two working days.
Request a proposal