MLOps Engineer

Our AI workloads are moving from early-stage deployment to production scale. You’ll design what they run on:

Re-architect how AI services are deployed and run — from single-host setups to horizontally scalable, orchestrated infrastructure that absorbs traffic spikes without degrading

Design for the specific realities of LLM-backed workloads: long-running requests, streaming responses, bursty concurrency, expensive downstream calls, and upstream rate limits

Own capacity, autoscaling, and unit economics — you should be able to say what we spend per unit of work, and why

Shipping AI changes should be a routine, reviewable event — not a coordinated risk:

Build the promotion path from development through pre-production to production, with environments that are consistent and reproducible rather than each one a special case

Make everything version-controlled and reviewable — infrastructure, configuration, and application logic on the same rails

Ship CI/CD that gives engineers fast deploys with real rollback, staged rollout, and change history you can audit

The highest-leverage thing you’ll build is the thing that lets other people ship without you:

Build internal tooling that lets engineers — and technically-minded teammates outside engineering — define, modify, and test AI workflow logic without touching deployment plumbing

Design the guardrails that make that safe: validation, versioning, review, staged promotion, and a clear path back when something is wrong

Treat internal users as customers. Success is measured in how many changes ship correctly without you being involved

You’ll care about whether the system actually holds — not just whether it deployed:

Instrument the AI stack end to end: latency, throughput, failure modes, cost, and output-quality signals

Build alerting that catches real degradation rather than noise, and incident practice that changes the system instead of assigning blame

Own secrets, access control, and environment isolation in a regulated industry where those things carry real consequences

Experience operating LLM or ML workloads in production — where cost, latency, and non-deterministic output are all live concerns

Cloud deployment experience (AWS preferred) — you can containerize, deploy, scale, and operate the systems you own

Experience in fintech, lending, insurance, or another regulated industry — secrets management, access control, audit trails, PII handling

Built internal developer platforms or self-serve tooling that non-infrastructure engineers actually used

Full-stack comfort — you can build a lightweight UI or internal tool when the platform needs one, rather than waiting for someone else to

Experience designing multi-environment promotion pipelines where configuration and logic move together with code

Familiarity with agent orchestration frameworks and what it takes to run them reliably

Observability for non-deterministic systems — tracing, evaluation signals, quality monitoring alongside standard telemetry

Inference optimization: model serving, batching, caching, and cost reduction strategies

Exposure to workflow automation tooling and low-code builders

Mortgage AI is still wide open. Unlike ad tech or e-commerce, where AI optimization is a rounding error, in mortgage lending an AI system that works changes whether a family gets their home. The problems are complex, the data is rich, and the solutions don’t exist yet.

The infrastructure problems here are real ones. Non-deterministic workloads, strict regulatory constraints, real financial stakes, and a company shipping AI changes faster than most infrastructure can absorb. This is not maintaining someone else’s platform — it’s designing the one everything else will be built on, at the moment when that decision matters most.

AI-first means AI-first. Every engineer here uses AI to build AI. Claude Code, agentic workflows, AI-assisted code review — we don’t add AI to our process, AI is our process. The team you’ll join is already operating at the frontier.

Direct leverage on everything we ship. Every AI feature this company builds runs on what you build. When deploys get faster and safer, every engineer here gets faster. When the platform scales, every loan we process moves quicker.