Senior AI Engineer
New
F
FutureFit AIAI workforce technology
Remote across the US and Canada. We are open to candidates living anywhere in either country.Full-TimeSenior
Salary150,000 - 185,000 USD per year
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Job Details
- Experience
- Roughly 5+ years
- Required Skills
- PythonSQLMachine LearningProduct DevelopmentPrompt Engineering
Requirements
- Have roughly 5+ years of applied ML/AI engineering experience and a track record of shipping systems into real products.
- Have built and shipped production LLM features, including prompt engineering, agentic workflows, tool use and function calling, retrieval, and orchestration.
- Have built LLM evaluations, defined quality metrics for open-ended output, and detected regressions.
- Have hands-on experience building and rigorously evaluating predictive or classification models, with a solid grounding in machine learning fundamentals.
- Be fluent in Python and SQL and comfortable working in production codebases.
- Have experience building ingestion and transformation pipelines, labeling data, maintaining knowledge sources, and applying data governance.
- Have experience connecting systems to APIs, databases, SaaS platforms, or third-party data providers, including handling delivery, data-quality, and authentication complexity.
- Use sound safety and guardrail practices for consequential user-facing systems.
- Be able to explain models, agent behavior, evaluation results, and tradeoffs to non-technical audiences.
Responsibilities
- Design, build, and ship LLM-based conversational coaching, agentic workflows, tool use, and orchestration.
- Build job-seeker coaching products that recommend next steps grounded in observed career transitions and evidence of impact.
- Build evaluation harnesses, offline and online quality measures, regression tracking, and human-in-the-loop review.
- Develop predictive, classification, and ranking models when appropriate for product needs.
- Prepare, label, govern, and maintain AI data pipelines and knowledge sources.
- Integrate AI systems with internal tools, databases, SaaS products, case management systems, and enterprise workflows.
- Establish prompting, evaluation, and agent-safety patterns for the team's AI work.
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