Staff ML Platform Engineer (MLOps)

FutureFit AI builds an AI-powered platform for workforce development and job seekers. The Staff ML Platform Engineer will build and operate the platform behind its machine learning and LLM-powered products, covering model deployment, evaluation, serving, infrastructure, and monitoring. The role also establishes reliable experimentation, observability, and shared operational standards for the data team.

Responsibilities:

• Assessment and plan: Evaluate our current pipelines, data architecture, and ML workflows, and produce a prioritized, opinionated plan for what needs to change
• Platform foundations and optimization: Own compute provisioning and environment management, keep training and serving environments reproducible, keep frameworks and packages current across services and model images, and tune latency, throughput, and spend, all without destabilizing production
• LLM infrastructure and smart routing: Build the layer our LLM features run on, including smart routing that sends each request to the cheapest model that can handle it well, plus the prompt and response evaluation needed to prove quality holds when we route down
• Experimentation and safe rollout: Give us a real discipline for A/B testing models before they are fully ramped: shadow deploys, canaries, holdouts, and success criteria agreed in advance, so a model earns its way into production instead of being switched on
• Observability and traceability: Know within minutes when a recommender goes down or starts drifting, and be able to explain why: model and data monitoring, alerting, regression detection, lineage, and enough traceability to reproduce a questionable recommendation on demand or trace a prediction back to the inputs that produced it (we currently use Braintrust; comparable tooling counts too)
• Hands-on operations: Stay close enough to the running systems to operate them. You will work with the team to keep models online, but when something breaks in production you can dive in and help fix it, including models other people built
• Data and feature infrastructure: Own how features are computed, stored, and served consistently between training and inference, and take on the data engineering the team needs along the way
• Standards, not sole ownership: Establish the deployment and monitoring standards the rest of the team can run with. You are building shared ownership, not becoming the only person who keeps things online