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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 as 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 impact job seekers' career trajectories. Your core responsibilities span multiple dimensions:
**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 form completion, application assembly), and the orchestration and tool-use layers that power them.
**Evidence-Grounded Coaching & Pathways**: Build products that guide job seekers to their next best career step. This includes conducting analysis on observed career transitions in labor market data, measuring causal impact on wages and lifetime earnings, and honestly assessing what your data can and cannot claim.
**Evaluation & Quality Ownership**: Establish how you measure AI system quality. You will build evaluation harnesses, offline and online quality measurement systems, regression tracking, and human-in-the-loop review processes that enable fast shipping without harm or inaccuracy.
**Applied ML Beyond LLMs**: Apply predictive, classification, and ranking models when appropriate, with sound judgment about when each tool is the right choice for the product problem.
**Data Engineering for AI**: Prepare, label, govern, and maintain the pipelines and knowledge sources that ground your AI systems in trustworthy data, ensuring retrieval and reasoning are accurate.
**Integration Engineering**: Connect AI systems safely to internal tools, databases, SaaS products, case management systems, and enterprise workflows, implementing necessary auth, guardrails, and failure handling for customer environments.
You will work on a small, high-ownership team, partnering closely with Engineering, Product, and the VP of Data & AI. This role carries real influence over AI development patterns: how the team prompts, evaluates, and decides what agents are permitted to do on users' behalf. You will help shape technical leadership across the AI platform and have the opportunity to grow into deeper technical leadership or broader product ownership.
The tech stack includes Python, SQL, modern LLM and agent tooling (e.g., LangChain, LangGraph), vector search and RAG frameworks, scikit-learn, AWS SageMaker, Airflow, dbt, PostgreSQL, Redshift, MongoDB, and visualization tools like Looker and Quicksight.
The role is remote across the US and Canada, with optional hybrid arrangements available at offices in Toronto (325 Front St West) and New York City (18 W 18th Street). You may be expected to travel up to once per quarter for off-sites and team gatherings.
**Requirements:**
- 5+ years of applied ML/AI engineering experience 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, build evals, define quality metrics for open-ended output, catch regressions before users do, and speak concretely about system failures and how you discovered them
- 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 providers) despite unreliable delivery and inconsistent quality
- Sound instincts about safety and guardrails in consequential domains; understanding that confidently wrong answers have real costs for users navigating benefits, eligibility, and career decisions
- Clear communication: ability to explain models, agent behavior, and evaluation results and tradeoffs to non-technical audiences
**Bonus Experience:**
- AWS and SageMaker, or broader AI system building and deployment on AWS
- Jobs-and-skills, HR, or labor market data; skills/occupation frameworks such as O*NET or 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 including retrieval quality evaluation
- Labor economics, program evaluation, or measuring impact of workforce interventions
- Publications, talks, blog posts, or open source work demonstrating depth in AI or ML engineering
