Staff ML Platform Engineer (MLOps)

The role

You will build the platform that ML and LLM-powered products run on. This includes owning the layer for model building, deployment, evaluation, serving, compute provisioning, and LLM routing. You will also handle observability, traceability, and data engineering for the platform.

What you will do

• Evaluate current pipelines, data architecture, and ML workflows to produce a prioritized plan.
• Own compute provisioning and environment management.
• Keep training and serving environments reproducible.
• Keep frameworks and packages current across services and model images.
• Tune latency, throughput, and spend.
• Build LLM infrastructure and smart routing for cost and quality.
• Implement experimentation disciplines like A/B testing, shadow deploys, canaries, and holdouts.
• Manage observability and traceability including model and data monitoring, alerting, and lineage.
• Perform hands-on operations and debug production ML incidents.
• Own data and feature infrastructure including pipelines and feature computation and storage.

Required qualifications

• Staff-level, hands-on experience in MLOps, ML platform, or ML infrastructure.
• Experience standing up MLOps practice end to end: CI/CD for models, experiment tracking, model registries, deployment workflows, and monitoring.
• Production experience with LLM-based systems: serving, prompt and response evaluation, routing across models and providers, and managing cost and latency tradeoffs.
• Experience operating models in both batch and real-time serving contexts.
• Hands-on with compute provisioning and environment management: containers, reproducible training and serving environments, and keeping frameworks and packages current.
• Experience running controlled model experiments in production: A/B tests, shadow or canary deploys, holdouts.
• Depth in observability and traceability for production ML: drift and regression detection, alerting, lineage, and prediction traceability.
• Hands-on operational experience: carrying the pager, debugging production ML incidents, and fixing systems not originally built.
• Comfort doing the data engineering the platform needs: pipelines, feature computation and storage.
• Track record of diagnosing and improving complex, fast-grown systems.
• Strong systems design ability.

Nice to have

• Interest in growing into model development yourself.
• Feature store experience.
• Experience evaluating AI/ML observability or LLM evaluation vendors.
• Background in mission-driven, workforce, or government-adjacent data environments.
• Comfort mentoring a small data and engineering team.

Benefits

• Remote-first company
• Benchmarked to the national market for comparable roles at institutionally-funded startups

Before you apply

• Candidates must have at least 5 to 6 hours of daily overlap with the EST-based team.
• The hiring process includes a performance challenge.

About the company

FutureFit AI is a SaaS/AI technology company whose mission is to help more people get to better jobs faster and cheaper. They focus on resolving economic inequality through an AI-powered platform for workforce development.