Senior AI Engineer

Senior AI Engineer role on FutureFit AI's Data team, remote across the US and Canada. The role builds AI products that connect job seekers to better jobs, including LLM-based coaching, agentic workflows, and orchestration grounded in labor market evidence. The role is hands-on and high-ownership on a small team, working closely with Engineering, Product, and the VP of Data & AI. Candidates must have at least 5 to 6 hours of daily overlap with an EST-based team. Travel may be expected up to once per quarter for off-sites and team gatherings. The hiring process includes an online application, an initial recruiter screen, a hiring manager interview, a performance challenge, final 1:1 interviews, and a final decision.

Responsibilities

Design, build, and ship LLM-based product experiences end to end, including conversational coaching, agentic workflows, and the tool use and orchestration layers underneath them; Build coaching and pathways products that guide job seekers to their next best step, and do the analysis behind those recommendations, including observed career transitions and causal impact on wages and lifetime earnings; Own evaluation and quality: build eval harnesses, offline and online quality measurement, regression tracking, and human-in-the-loop review; Build predictive, classification, and ranking models when they are the right tool for the product problem; Prepare, label, govern, and maintain the pipelines and knowledge sources AI systems depend on; Integrate AI systems safely with internal tools, databases, SaaS products, case management systems, and enterprise workflows, including auth, guardrails, and failure handling

Requirements

Strong applied ML/AI engineering experience (roughly 5+ years), with a track record of shipping systems into real products used by real people; Demonstrated LLM product experience, including building and shipping LLM-based features to production with prompt engineering, agentic workflows, tool use and function calling, retrieval, and orchestration; Real evaluation discipline: building evals, defining quality metrics for open-ended output, catching regressions, and explaining where systems failed; Classical ML depth: solid ML fundamentals and hands-on experience building and rigorously evaluating predictive or classification models; Fluency in Python and SQL with real comfort in production codebases; Experience building data and integration foundations (ingestion and transformation pipelines, labeling, knowledge sources, governance, and connecting to APIs, databases, SaaS platforms, and third-party data providers); Sound instincts about safety and guardrails in a consequential domain; Clear communication of models, agent behavior, and evaluation tradeoffs to non-technical audiences

Nice to have

Experience with AWS and SageMaker, or building and deploying AI systems on AWS; Experience with jobs-and-skills, HR, or labor market data, or skills/occupation frameworks such as O*NET or ESCO; Experience integrating with case management systems, government systems, or public-sector data environments; Experience with recommender systems, ranking, or search at scale; Experience with retrieval-augmented generation at production scale, including retrieval quality evaluation; Experience with labor economics, program evaluation, or measuring the impact of workforce interventions; Publications, talks, blog posts, or open source work showing depth in AI or ML engineering