Senior/Staff ML Ops Engineer

Filevine is a Legal AI company delivering Legal Operating Intelligence for the future of legal work. Grounded in a singular system of truth, Filevine brings together data, documents, workflows, and teams into one unified platform—where modern legal work happens with clarity and consistency.

Powered by LOIS, the Legal Operating Intelligence System, Filevine connects context across every matter to transform legal operations from reactive to proactive. LOIS reads, understands, and reasons across your data to surface insight, automate complexity, and give professionals the clarity and confidence to see more, know more, and do more. Fueled by a team of exceptional collaborators and innovators, Filevine’s rapid growth has earned AI awards and recognition from Deloitte and Inc. as one of the most innovative and fastest-growing technology companies in the country.

Role Summary: As a Senior MLOps Engineer at Filevine, you'll sit at the intersection of machine learning, platform infrastructure, and product velocity. You'll build and own the systems that make Filevine's AI capabilities faster to develop, safer to ship, and easier to trust, at scale. You will be responsible for the full stack of ML infrastructure: evals, observability, model serving, annotation tooling, and the prompt platform that lets every team move with confidence.

Setup and maintain LLM observability frameworks/tools Help improve data annotation tooling Ensure stability of LLM calls (rate limits, provisioned throughput, backups, …) Help to drive security review processes for AI vendors and providers LLM cost optimization recommendations (caching, batching, identification of workflow parts causing high costs, etc.) Hosting finetuned/open weight machine learning models Helping with LLM evaluations (tooling/framework) with the current main focus on agentic evals Platform tooling for enabling non-technical people (e.g. PMs) to iterate on prompts

5+ years building and operating software systems end-to-end Hands-on experience with ML infrastructure: model serving, training pipelines, or LLM integrations in production Strong understanding of cloud infrastructure and distributed systems (primarily AWS) Familiarity with observability tooling and cost management for LLM workloads Experience with or openness to: Python, Kubernetes, Terraform Thrives in a remote-first, async environment: clear communicator, high ownership, low ego Bonus: experience with eval frameworks, annotation tooling, or prompt management platforms

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