Lead Applied AI Engineer

Role overview Build and operate AI assistants, agents, and automated workflows that help business teams make decisions and handle operational tasks. The role focuses on putting generative AI into production with governed company data, working across data, IT, and business teams rather than doing model research.

Responsibilities - Develop internal assistants and copilots, using retrieval augmented generation grounded in structured data and curated business metrics. - Automate processes such as ticket triage, routing, approvals, and document handling, with human review and exception paths where needed. - Build AI services, APIs, and microservices and connect them to operational business systems. - Use data models and business definitions to support batch and real-time decision workflows. - Create serverless cloud workflows and monitor reliability, latency, usage, accuracy, and cost. - Add safeguards, evaluation, logging, audit records, and traceability; improve solutions using outcomes and user feedback.

Requirements - At least five years of software or machine learning engineering experience, including two or more years building production LLM systems. - Experience designing, deploying, and maintaining AI-powered services and integrations. - Ability to work across data pipelines, orchestration, and model inference to diagnose production issues. - Experience applying prompt strategies, guardrails, and response evaluation to business workflows. - Ability to collaborate with data, IT, and business stakeholders and contribute to technical design discussions.

Benefits and work setup The listed salary range is $160,000–$180,000, with the final amount depending on experience, education, and location. Benefits listed include health, dental, life, disability, paid time off, and a 401(k) plan.