ML Ops Lead

Role Description

We're seeking a Staff / Principal MLOps Engineer to join our team. Our data and ML infrastructure has grown fast alongside the business, and it now needs senior ownership to bring it to where it should be. You will come in, assess the state of our pipelines and data architecture with clear eyes, decide what to fix and in what order, and then go fix it yourself. This is a role for someone senior enough to write systems designs grounded in what our customers actually need, and hands-on enough to be in the codebase implementing them. We are open to running this as a six-month contract or as a full-time hire, depending on fit and what you are looking for.

• Assessment and plan: Evaluate our current pipelines, data architecture, and ML workflows, and produce a prioritized, opinionated plan for what needs to change and why.
• Systems design: Design data and ML systems that are anchored in customer needs and built to last, with clear tradeoffs documented so the team can build on them.
• Hands-on implementation: Do the work: rebuild and harden pipelines, upgrade the data architecture, and ship the fixes yourself rather than handing off a deck.
• Reliability and standards: Raise the bar on observability, reliability, and data quality, establishing the patterns and practices the rest of the team can run with.

Qualifications

• Staff or principal-level experience in MLOps, data engineering, or ML platform/infrastructure.
• A track record of walking into complex, fast-grown systems, diagnosing the real problems, and materially improving them.
• Strong systems design ability: you can translate customer and product needs into durable, scalable architecture and communicate it clearly.
• Genuinely hands-on: you are as comfortable in the codebase implementing the fix as you are in the design doc.
• Deep experience building and operating production data pipelines and ML workflows at scale.
• Fluency with the modern data and ML stack and the cloud infrastructure it runs on.

Requirements

• Experience standing up MLOps practice (CI/CD for models, experiment tracking, feature stores, monitoring) from an early stage.
• Background in mission-driven, workforce, or government-adjacent data environments.
• Publications, presentations, blog posts, or other public artifacts showcasing your expertise and knowledge of best practices in MLOps.
• Comfort mentoring and leveling up a small data and engineering team while you build.

Benefits

• Open to engaging this role as a six-month contract with potential to convert, or as a full-time hire.
• Base salary range for full-time path: USD $195,000 to $245,000 for candidates based in New York and CAD $175,000 to $220,000 for candidates based in Toronto.
• Contract path rate range: USD $120 to $170 per hour, commensurate with level.

Company Description

At FutureFit AI, our core mission is to help more people get to better jobs faster and cheaper, with a specific focus on those facing barriers to opportunity. Our work helps resolve the growing issue of economic inequality, ensuring that no one is left behind in the future of work. Our AI-powered platform brings efficiency and insight to workforce development, replacing outdated systems and unlocking human potential at scale.

• Team: 30-50 across US and Canada (hubs in NYC and Toronto).
• Customers: Workforce development agencies and intermediaries, government agencies, employers.
• Industry: SaaS/AI technology.
• Funding: Bootstrapped 0-1, then raised funding led by JP Morgan.
• Structure: Growth, Customer Success, Product, Engineering, Data, People & Culture, Finance & Operations.