ML Ops Lead

Staff / Principal MLOps Engineer Contract (6 months, potential to convert) or Full-Time | Remote (US or Canada) Come join our Data team! High velocity, high intensity, high trust, high bar, high impact, and a will to win. If those words resonate deeply with you, this could be your next career move. We're seeking someone who leads with humility, pursues audacious goals, and is motivated by meaningful impact on people and the world. 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 Our AI-powered platform brings efficiency and insight to workforce development, replacing outdated systems and unlocking human potential at scale. Ready to make an impact? Apply today. Important note: Data shows that men typically apply when meeting 3/10 requirements, while women often wait until it's 10/10. We encourage you to apply if you see a strong (not necessarily perfect) fit. Your Role We're seeking a Staff / Principal MLOps Engineer to join our team. Our ML footprint has grown quickly alongside the business: batch models that process records in the backend, real-time models that serve recommendations to job seekers, and daily pipelines that process every available job across the US and Canada. The layer we have not yet built is the observability and traceability around all of it. Today, when a model regresses, a job fails, or a recommendation looks wrong, especially where LLMs are involved, tracing the cause and reproducing it takes far longer than it should. You will own that problem: assess our ML pipelines and data architecture with clear eyes, decide what to build and in what order, and then build it. This is a greenfield mandate, influencing production models and users. 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. What You'll Own Assessment and plan: Evaluate our current pipelines, data architecture, and ML workflows, and produce a prioritized, opinionated plan for wh at needs to change and why. AI/ML observability: Architect our AI/ML observability and traceability from the ground up: model and data monitoring, regression detection, lineage, and the ability to reproduce a questionable recommendation on demand, including for LLM-based systems. 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. Implementation: Rebuild and harden pipelines, upgrade the data architecture, and ship the improvements. Reliability and standards: Raise the bar on reliability and data quality, establishing the patterns and practices the rest of the team can run with.