Staff ML Platform Engineer (MLOps) – Remote

FutureFit AI; United States; Remote; USD 172,000 - 215,000; Full time

Job summary

FutureFit AI is hiring a Staff ML Platform Engineer to build the platform our ML and LLM-powered products run on. You’ll own that layer end to end: how models get built, deployed, evaluated, and served; how compute and environments get provisioned and managed; how our LLM calls get routed and optimized for cost; and how we know quickly when a recommender goes down or goes off the rails.

This is a build role and an operate role: when a model regresses or a recommendation looks wrong, you can

Qualifications

• Staff-level, hands-on experience in MLOps, ML platform, or ML infrastructure.
• End-to-end MLOps with CI/CD for models, experiment tracking, and deployment workflows.
• Production experience with LLM-based systems including routing and cost management.
• Experience in both batch and real-time serving contexts.
• Strong data engineering and feature infrastructure capabilities.
• Track record of diagnosing complex, fast-growing systems and improving them.

Responsibilities

• Assess current pipelines and plan changes with an opinionated, prioritized approach.
• Own compute provisioning and environment management to keep training/serving reproducible.
• Build the LLM infrastructure and smart routing to minimize cost while maintaining quality.
• Lead safe rollout practices: shadow deploys, canaries, and clear success criteria.
• Ensure observability, drift detection, and traceability for production ML.
• Operate and debug production systems; collaborate with teams to fix issues.
• Own data and feature infrastructure to ensure consistency between training and inference.
• Establish deployment and monitoring standards for shared ownership.

Skills

MLOps

ML Platform

CI/CD for models

LLM-based systems

Observability & tracing

Hands-on operations

Data engineering

Tools

Airflow

dbt

PostgreSQL

Redshift

MongoDB

SageMaker

Looker

Quicksight

Job description

FutureFit AI is hiring a Staff ML Platform Engineer to build the platform our ML and LLM-powered products run on. You’ll own that layer end to end: how models get built, deployed, evaluated, and served; how compute and environments get provisioned and managed; how our LLM calls get routed and optimized for cost; and how we know quickly when a recommender goes down or goes off the rails.

This is a build role and an operate role: when a model regresses or a recommendation looks wrong, you can