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.

Responsibilities

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

Qualifications

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