Senior AI Engineer

Role overview

Build production AI products that help people navigate career options and take practical steps toward better employment. The work combines LLM-powered coaching and task automation with evidence-based recommendations, applied machine learning, and reliable integrations into workforce and case-management systems. This is a hands-on role with broad technical ownership on a small team.

Responsibilities

- Design, build, and ship conversational coaching, agentic workflows, and the tool-use and orchestration layers behind them. - Develop recommendations for career pathways using observed job transitions and measured outcomes, while clearly communicating what the data can and cannot support. - Establish AI quality practices, including evaluation harnesses, offline and live measurement, regression monitoring, and human review. - Build predictive, classification, or ranking models when they are a better fit than an LLM. - Prepare and maintain trusted data pipelines, labeled datasets, and knowledge sources for retrieval and reasoning. - Integrate AI systems with internal tools and external platforms, including suitable access controls, safeguards, and failure handling.

Requirements

- Experience shipping LLM features to production, including prompt design, retrieval, tool use, function calling, and multi-step workflows. - Demonstrated ability to evaluate LLM systems, define quality measures, detect regressions, and learn from failures. - Strong machine learning fundamentals and hands-on experience building and rigorously evaluating predictive or classification models. - Fluency in Python and SQL and comfort contributing to production codebases. - Experience building data ingestion and transformation pipelines, labeling and governing knowledge sources, and integrating APIs, databases, SaaS, or enterprise systems. - Good judgment about safety and safeguards when AI supports consequential decisions.

Nice to have

- Interest in technical leadership or broader product ownership as the AI platform and coaching products evolve.

Benefits and work setup

- The role works closely with engineering, product, and data leadership, with room to shape technical patterns and take on unfamiliar work. - The hiring process may include an initial recruiter screen, a hiring-manager interview, a practical challenge, and final interviews; the stated typical duration is about six weeks, subject to variation.