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Job description
You will own AI features end to end, from the first evaluation harness to the system running in a client’s cloud. We build copilots, agents, and RAG systems for regulated industries, so correctness, observability, and graceful failure matter as much as the happy path.
This is a hands-on engineering role on a small, senior team. You will write the hard parts yourself, set the patterns others follow, and stay close to the decisions that determine whether a build ships or stalls.
What you'll do
Architect and build LLM applications: agents, tool use, retrieval, and evaluation pipelines.
Stand up evals and observability so quality is measured continuously, not guessed at.
Harden systems for production: latency, cost, prompt-injection, data leakage, and fallbacks.
Integrate with messy client data and existing systems without breaking their controls.
Set engineering patterns and review work across the team.
What we're looking for
5+ years building production software, with recent hands‑on LLM/ML application work.
Strong Python and TypeScript; comfortable across the full stack of an AI system.
Real experience with evaluation, retrieval, and the failure modes of LLM systems.
A bias for shipping something narrow that works over something broad that demos.
Clear written communication; you can explain trade‑offs to engineers and executives.
Nice to have
Experience deploying into client VPCs / on‑prem with SSO and data‑residency constraints.
Background in a regulated sector (finance, healthcare, legal, real estate).
Open‑source contributions or published technical writing.
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Who can apply
Review the original listing for work authorization, qualifications and employer requirements.