A Day in the Forward Deployed AI Engineer Life
How I operate inside customer engineering teams, security reviews, and production clusters to deliver working AI systems.
A deployment day, hour by hour
On site before the team. Read yesterday's run log and the overnight errors — I want to know what broke before anyone tells me.
CheckStand with the operators. Watch the actual workflow being done, not the version in the process document. Note every exception they handle by hand.
ObserveWhiteboard with whoever owns the data. Where does this field come from, who is allowed to see it, and what happens today when it is missing?
ModelBuild. Wire the tool, extend the agent, fix the retrieval that returned the wrong policy version this morning.
BuildRun it against real cases from their own data. Add the ones that fail to the eval set so the same mistake cannot come back quietly.
EvaluateShow the operator, not the sponsor. Sit next to them while they use it and write down every hesitation — that is the real backlog.
ShowRebuild what they hesitated over. Small, same-day changes are what earn the right to keep coming back.
IterateWrite it down. What changed, what it cost, what is still open — a short note the sponsor can read in a minute and the next engineer can read in ten.
RecordOperating Rules I Work By
Ship something every day
A working ugly thing today beats a beautiful design next week. Momentum is what keeps an engagement alive.
Show the operator first
The person doing the work knows things no engineer will. If it does not survive their desk, it will not survive the rollout.
Escalate rather than guess
Below the confidence threshold, the system asks a human. In a regulated estate a wrong answer costs more than a slow one.
Leave evidence
Every run logs its prompts, tools, results and errors. If an auditor cannot replay it, it is not finished.
Build to hand over
The engagement ends with their team running it — documented, tested and owned by someone who is not me.
Discover, Prototype, Show & Iterate
On site. Back-to-back stakeholder interviews. Understand the workflow and who owns each step of it.
DiscoverWhiteboard the domain. Sketch the object model. Identify the datasets the system actually needs.
ModelPair with their data engineer to pull the first export. Stand up the pipeline and the retrieval layer.
PlumbBuild the first working version of the operator's app and put it in front of them the same afternoon.
PrototypeThey disliked the layout. Rebuild it, then show the sponsor something that runs end to end before the week closes.
Iterate