Senior AI Engineer - LLM Coaching Platform

FutureFit AI; United States; Remote; USD 150,000 - 185,000; Full time

Job summary

FutureFit AI in the United States (remote across the US and Canada) is seeking a Senior AI Engineer to build AI products that sit between a job seeker and their next opportunity. You will own LLM-based coaching, agentic workflows, and the orchestration that pulls context from case management systems to deliver grounded, actionable guidance.

This hands-on role emphasizes evaluation, safety, and data engineering, with close collaboration across Engineering, Product, and Data & AI leadership.

Qualifications

• 5+ years of applied ML/AI engineering experience in real products.
• Built and shipped LLM-based features to production, including prompt engineering and tool usage.
• Experience with evaluation metrics for open-ended outputs and regressive risk.
• Solid fundamentals in machine learning; know when to use LLMs vs other tools.
• Fluency in Python and SQL with production code experience.
• Experience building data/integration foundations: pipelines, labeling, governance.
• Ability to explain model behavior and evaluation results to non-technical audiences.

Responsibilities

• Design, build, and ship LLM-based product experiences end to end.
• Develop coaching and pathways grounded in evidence with measurable outcomes.
• Own evaluation harnesses, offline/online quality metrics, and regression tracking.
• Develop non-LLM predictive/classification models when appropriate.
• Build data engineering pipelines and governance for AI systems.
• Create safe integrations with internal tools, databases, and external systems.

Skills

ML/AI engineering

LLM experience

Python

SQL

Model evaluation

Safety & guardrails

Communication

Tools

SageMaker

LangChain

LangGraph

Python tooling

Job description

FutureFit AI in the United States (remote across the US and Canada) is seeking a Senior AI Engineer to build AI products that sit between a job seeker and their next opportunity. You will own LLM-based coaching, agentic workflows, and the orchestration that pulls context from case management systems to deliver grounded, actionable guidance.

This hands-on role emphasizes evaluation, safety, and data engineering, with close collaboration across Engineering, Product, and Data & AI leadership.