Most AI projects don’t fail at the model. They fail somewhere between the demo that impressed everyone and the system that has to survive contact with a real customer’s data, workflows, and definitions.
I published a new LinkedIn Learning course on the role that closes that gap: Forward Deployed Engineering in the Age of AI.
Why This Course Exists
AI deployment differs fundamentally from a traditional SaaS rollout. A SaaS product is largely the same on day one for every customer. An AI system is not — its behavior depends on the customer’s data quality, their vocabulary, their edge cases, and their tolerance for being wrong. That means the work of making it succeed happens in the field, not at headquarters.
Forward deployed engineering is how that work gets done. It stopped being optional the moment companies started shipping systems whose output has to be trusted.
What the Course Covers
This 36-minute course walks through:
- Why forward deployment became essential rather than optional as AI moved from prototypes into production
- Context engineering as an FDE responsibility — and how it differs from prompt engineering
- Why FDEs have to be developed internally rather than hired off the market
- How field experience reshapes engineering judgment about data quality and evaluation
Who This Is For
Engineers moving into customer-facing AI work, technical leaders standing up an FDE function, and anyone responsible for getting an AI system past the pilot stage. The course is beginner-level and assumes no prior FDE experience.
Background
I built and led Project Frontline at Palantir, training 250+ engineers in this methodology. Alumni now work at OpenAI, xAI, Anduril, and other AI companies. I later led FDE practices at Citadel before co-founding Kepler.
If you want the longer written treatment, see The Definitive Guide to Forward Deployed Engineering.
You can find the course on LinkedIn Learning.
Questions about FDE or deploying AI in the field? Feel free to reach out.