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Salary: USD 150,000 - 185,000 / annual
FutureFit AI is building AI-powered workforce development technology to help job seekers access better opportunities faster and cheaper, with a focus on those facing barriers to economic mobility. The company operates a SaaS/AI platform serving workforce development agencies, government agencies, and employers across the US and Canada.
As a Senior AI Engineer, you will own the design, development, and deployment of LLM-based products that directly serve job seekers. Your core responsibilities include:
**LLM Product Development**: Design and ship end-to-end LLM-based experiences including conversational coaching systems, agentic workflows that complete multi-step tasks (e.g., eligibility forms, application assembly), and the orchestration and tool-use layers that ground these interactions in real data.
**Evidence-Grounded Coaching & Pathways**: Build products that guide job seekers to their next best career step. You will own both the analysis (observed career transitions, causal impact measurement on wages and lifetime earnings) and the engineering that translates evidence into usable product recommendations.
**Evaluation & Quality Assurance**: Establish how the team measures AI quality. You will build evaluation harnesses, offline and online quality metrics, regression tracking, and human-in-the-loop review processes that enable fast shipping without harm or inaccuracy.
**Applied ML Beyond LLMs**: Develop predictive, classification, and ranking models when appropriate, with the judgment to select the right tool for each product problem.
**Data Engineering for AI**: Prepare, label, govern, and maintain the data pipelines and knowledge sources that ground retrieval and reasoning in trustworthy data.
**Integration Engineering**: Connect AI systems safely to internal tools, databases, SaaS products, case management systems, and enterprise workflows, with proper authentication, guardrails, and failure handling.
This is a high-ownership, hands-on role on a small team. You will partner closely with Engineering, Product, and the VP of Data & AI, with wide latitude to shape how these systems are built. You will help establish patterns for prompting, evaluation, and agent authorization across the team. Career growth paths include deeper technical leadership over the AI platform or broader product ownership of coaching and pathways.
The company is 30–50 people across US and Canada, with offices in NYC and Toronto. Although remote, you may be expected to travel up to once per quarter for off-sites and team gatherings. The tech stack includes Python, SQL, modern LLM and agent frameworks (LangChain, LangGraph), scikit-learn, AWS SageMaker, Airflow, dbt, PostgreSQL, Redshift, MongoDB, and Looker/QuickSight.
**REQUIREMENTS**
- Strong applied ML/AI engineering experience (approximately 5+ years) with a track record of shipping systems into real products used by real people
- Demonstrated LLM product experience: built and shipped LLM-based features to production (not prototypes), including prompt engineering, agentic workflows, tool use and function calling, retrieval, and orchestration
- Real evaluation discipline: ability to measure whether LLM systems work, including building evals, defining quality metrics for open-ended output, catching regressions before users encounter them, and concrete understanding of system failures and how they were discovered
- Classical ML depth: solid grounding in machine learning fundamentals with hands-on experience building and rigorously evaluating predictive or classification models (firm requirement)
- Fluency in Python and SQL with real comfort in production codebases
- Experience building data and integration foundations for AI: ingestion and transformation pipelines, labeling, knowledge sources, governance, and connecting to external/enterprise systems (APIs, databases, SaaS platforms, third-party data) despite reliability and quality challenges
- Sound instincts about safety and guardrails in consequential domains; understanding that incorrect answers about benefits, eligibility, and career decisions have real costs
- Clear communication: ability to explain models, agent behavior, and evaluation results and tradeoffs to non-technical audiences
**BONUS EXPERIENCE**
- AWS and SageMaker experience, or broader AI system deployment on AWS
- Jobs-and-skills, HR, or labor market data experience; familiarity with skills/occupation frameworks (O*NET, ESCO)
- Integration with case management systems, government systems, or public-sector data environments
- Recommender systems, ranking, or search at scale
- Production-scale retrieval-augmented generation (RAG) including retrieval quality evaluation
- Labor economics, program evaluation, or measuring workforce intervention impact
- Publications, talks, blog posts, or open source work demonstrating AI/ML engineering depth
