Senior AI Engineer

The Senior AI Engineer leads the design, delivery, and production operation of complex healthcare AI products spanning traditional software enhanced by AI to AI-native, agentic workflows. This hands-on role owns prompts, agent behavior, retrieval, model optimization, evaluations, and human-review controls that make high-stakes workflows safe, reliable, observable, and governable.

Working independently and in close partnership with Product, business, clinical, operational, Data Engineering, Platform, and Security teams, the Senior AI Engineer translates real-world workflows into configuration-driven decision logic and scalable systems for operating AI at enterprise scale. They curate domain datasets, drive evaluation-based iteration, optimize models and retrieval, and help advance what is possible with AI in a highly regulated healthcare environment.

This is a work-from-home position; candidates located in TX, MN, or FL are preferred, subject to business need.

Duties & Responsibilities

• Lead the end-to-end delivery of complex AI products including agentic workflows, RAG systems, and services.
• Collaborate with Product, Engineering, Data, and clinical and operational teams.
• Translate stakeholder needs into validated, access-aware AI workflows with clear system behavior, acceptance criteria, risk controls, and delivery trade-offs.
• Design AI workflow and RAG product lines, including ingestion, retrieval quality, attribution, confidence handling, agent tools, and human-review paths.
• Implement workflows with reliable AI control layers and optimize healthcare AI models across prompting, retrieval, reranking, fine-tuning, and model-selection approaches.
• Own healthcare AI evaluation and release frameworks, including adversarial testing, regression thresholds, release gates, and production telemetry.
• Provide senior technical leadership through design and code reviews, incident response, mentoring, and reusable engineering improvements.
• Deliver healthcare AI services using approved cloud capabilities and HITRUST-aligned controls for PHI/PII, security, privacy, auditability, responsible AI, and compliance.

Education, Training, and Professional Experience

• A Bachelor’s degree in Computer Science, Computer Engineering, Information Systems, Data Science, Linguistics, or a related field; equivalent practical experience may be considered.
• Typically, 5+ years of relevant engineering experience, including ownership of complex production components or technical leadership of feature delivery.
• Hands-on experience delivering and operating production or near-production AI/ML/LLM-enabled applications.
• Strong software engineering expertise in service and data-contract design, testing, CI/CD, observability, debugging, reliability, and operational support.
• Strong proficiency in Python, Java, Scala, C#, TypeScript, or a comparable modern language.
• Deep working knowledge of structured outputs, RAG, tool use, evaluations, guardrails, monitoring, and human-in-the-loop design.
• Experience with cloud services and data-intensive systems; ability to collaborate across structured-data products and unstructured-data pipelines.
• Demonstrated self-directed exploration of emerging AI capabilities through independently built prototypes, tools, experiments, or applications.
• Experience translating AI experimentation into production solutions for enterprise or regulated environments, with attention to safety, reliability, security, governance, and measurable outcomes.

Preferred Experience

• Healthcare experience involving PHI/PII, clinical or operational workflows, payer/provider operations, auditability, safety review, compliance controls, or value-based care.
• Experience with cloud AI and data platforms such as Azure, AWS, GCP, Databricks, Agent Bricks, Genie, Azure AI Foundry/Azure OpenAI, Azure ML, AKS/Kubernetes, OpenTelemetry, or AI observability and evaluation tooling.
• Model benchmarking, selection, adaptation, fine-tuning, reranking, or other domain-model optimization techniques.
• Experience leading technical initiatives across teams or mentoring several engineers.