Senior ML Engineer

ABOUT THE ROLE We're hiring a Senior ML Engineer to be the data team's owner of production ML and AI agent systems. You'll take models from prototype to production, build and maintain the low-latency ML API that powers our Next Best Action (NBA) engine, and partner with our HAL team to ship LLM agents that turn NBA recommendations into real conversations with credit union members and partners. This is a builder's role at a builder's moment: NBA is going live, the agent infrastructure is being shaped now, and you'll define how Clutch does production AI for years to come. ABOUT THE TEAM The Data team today is five people: one data scientist, two data engineers, one data analyst, and one product manager. We're small, ambitious, and shipping fast — two ML models heading to production, an ML API being built, and two AI agents (one customer-facing, one partner-facing) in active development. You'll be the senior technical voice for ML and AI engineering inside the team, and the bridge to HAL, the platform team that builds Clutch's agent runtime. Expect tight feedback loops, real autonomy, and a team that values pragmatism over purity. WHAT YOU’LL DO WITHIN 3 MONTHS, YOU WILL: - Take ownership of the ML API that serves NBA recommendations, partnering with the data engineer who's been building it, and harden it for low-latency production traffic - Ship your first agent tool contract end-to-end: schema design, handler implementation, structured-error contract, unit tests, deployed via HAL's runtime - Set up the eval foundation for our agents: golden transcripts, rubric-based judges, regression suites that run on every prompt or model change - Build a working relationship with HAL and become the data team's go-to on agent infrastructure decisions WITHIN 6 MONTHS, YOU WILL: - Be the primary owner (with data engineer support) of the ML API and the agent tool layer that wraps NBA and our ML models - Have shipped at least one production-grade agent (customer-facing or partner-facing) with prompt versioning, evals, observability, and multi-tenant gating in place - Define the data team's playbook for shipping a new ML model as an LLM-callable tool, end-to-end - Mentor the data engineers on ML/AI patterns so they can confidently support and extend the systems you own WITHIN 9 MONTHS, YOU WILL: - Operate as the technical lead within the data team for NBA production AI at Clutch — the person other teams come to when they want to understand how NBA ships ML and agents responsibly - Have measurably improved agent cost and latency (target: 30%+ reduction on P95 latency or per-conversation cost on at least one agent) - Be shaping the data team's roadmap for the next generation of ML and AI products, in partnership with the PM and data scientist - Help us decide what to hire next as the team scales WHAT YOU’LL BRING Required - 7+ years of engineering experience, with a proven track record of building and shipping production ML systems — you've taken models from prototype to production and own what happens after deploy - Strong Python — most of the work (ML training, evaluation, the ML API, data pipelines) is in Python, and you're comfortable in production codebases, not just notebooks. Some TypeScript is involved for tool contracts and integration with our agent runtime — you don't need to be an expert, comfort with a second language is enough - Tool-design discipline for LLM consumption. Can take an ML model or data source and shape it into an LLM-callable tool with narrow input/output schemas, identity-required and scope-gated dispatch, and structured-error contracts (RATE_LIMITED, UPSTREAM_ERROR, NOT_FOUND) that the agent runtime converts to graceful tool-results instead of crashing - Eval discipline for non-deterministic systems. You treat evals as the unit-test equivalent for agents: golden transcripts, rubric-based judges, regression suites that run on every prompt or model change. You understand the difference between offline metrics and online