Senior, AI native, working in your repo and your standup. Scoped to deliverables, not day rates.
No body shop, no bench, no anonymous CVs. You don't manage a vendor relationship, and you never get a mystery substitute.
Someone who has put LLM systems into production, dropped into your team, contributing from the first week and scaling with your priorities.
Code in your repo, infra in your cloud, docs in your wiki. If we leave tomorrow, nothing leaves with us.
Multi-step agents with tools, approvals and audit trails, built for production, not demos.
Retrieval over your real documents with evals that prove it answers correctly.
Test harnesses for LLM behavior, so changes ship with evidence instead of vibes.
Assistants, copilots and automation features wired into your existing codebase.
Model routing, caching, cost control and observability that survives real traffic.
Support and sales conversations over WhatsApp, web and voice that resolve, not deflect.
Access sorted, environment running, introductions done. The engineer reads code, not slide decks.
Something small and real: a fix, a harness, a piece of plumbing the roadmap needs.
Estimating in your planning, reviewing your team's code, shipping on your cycle like anyone else on payroll.
The AI feature you hired for is live, documented, and your team knows how it works inside out.
Hiring an AI engineer takes months and a salary you commit to before you know what you need. This starts next week, scoped to the work in front of you.
There is no handoff because there is no outside. The engineer works in your repo, your standup and your review process from day one.
That's the point. They pair, review and document so your team levels up on AI work instead of depending on us forever.
Say so in week one and you pay nothing further. Fit risk is ours to carry, not yours.
One conversation. If an embedded engineer is the wrong answer, you'll hear that too.