Understand the workflow. Build the useful system.

I’m pursuing remote Forward Deployed AI Engineer and AI implementation opportunities that connect customer context with hands-on software delivery. My foundation combines enterprise application work, independent US client engagements and public AI/web projects.

A practical delivery approach

01 · Discover

Understand the operator

Map the task, users, existing tools, data access and the points that need human judgment.

02 · Scope

Define a useful slice

Agree on a small outcome, assumptions, acceptance criteria, risks and an implementation path.

03 · Prototype

Make the idea reviewable

Build a demo around the actual workflow; use feedback to test whether the solution is useful.

04 · Integrate

Connect the real system

Bring interfaces, APIs and data flows together with explicit error states and access boundaries.

05 · Verify

Test the important behavior

Check contracts and user flows; define evaluation examples where model output affects decisions.

06 · Handover

Make delivery maintainable

Document setup, deployment and known limitations so the team can review and operate the work.

AI-assisted building, with review

Claude Code, Codex and Cursor support my implementation workflow. ChatGPT, Claude and Gemini help with investigation and iteration. The engineering responsibility remains with me: inspect the code, validate the result and explain the tradeoffs.

My REA-inspired approach starts from understanding an existing workflow or authorized reference, then building a scoped demonstration. Access, intellectual property and data boundaries are agreed with the client.

Forward Deployed Engineer is a target role and delivery direction. I do not claim a previous formal FDE job title or unverified customer outcomes.

One conversation, a concrete next step

Let’s build something useful.

Send a role or a short project brief. We can discuss the problem, location eligibility, stack, scope and availability.

Discuss a role or project ↗Inspect my work

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