Senior AI Engineer

Role overview Lead the design, delivery, and ongoing operation of AI products for complex healthcare workflows. The work spans software enhanced by AI and agent-based systems, with a focus on making model behavior, retrieval, evaluation, and human review safe and dependable in a regulated setting. The role partners closely with product, engineering, data, clinical, operational, platform, and security teams.

Responsibilities - Own end-to-end delivery of AI services, retrieval-augmented generation systems, and agent workflows. - Translate stakeholder needs into validated, access-aware workflows with clear behavior, acceptance criteria, and risk controls. - Design data ingestion, retrieval, source attribution, confidence handling, agent tools, and human-review paths. - Improve system performance through prompt design, retrieval, reranking, model selection, and fine-tuning approaches. - Establish evaluation and release practices, including adversarial checks, regression thresholds, release gates, and production monitoring. - Provide technical leadership through design and code reviews, incident response, mentoring, and reusable engineering improvements. - Deliver services using approved cloud capabilities and controls for sensitive information, security, privacy, auditability, and compliance.

Requirements - Bachelor’s degree in a relevant technical or related field, or equivalent practical experience. - Typically five or more years of relevant engineering experience, including ownership of complex production components or technical leadership. - Hands-on experience delivering and operating production or near-production AI, machine learning, or LLM applications. - Strong software engineering skills across service and data contracts, testing, CI/CD, observability, debugging, reliability, and operational support. - Proficiency in Python, Java, Scala, C#, TypeScript, or a comparable modern language. - Working knowledge of structured outputs, RAG, tool use, evaluation, guardrails, monitoring, and human review. - Experience with cloud services and data-intensive systems, including collaboration across structured and unstructured data. - Ability to turn AI experimentation into secure, reliable, governed production solutions.

Nice to have - Healthcare experience involving sensitive data, clinical or operational workflows, payer or provider processes, safety review, or compliance. - Familiarity with cloud AI and data platforms, Kubernetes, and AI observability or evaluation tooling. - Experience with model benchmarking, adaptation, fine-tuning, reranking, or other domain optimization. - A record of leading cross-team initiatives or mentoring engineers.

Benefits and work setup - Work from home. Candidates in Texas, Minnesota, or Florida are preferred, subject to business need.