Director, AI & ML (US - Remote)

Role Overview & Scope Care Lumen is building an AI-first healthcare platform serving complex payer workflows across Care Management, Utilization Management, and LTSS. We are seeking a Director of AI & Machine Learning to establish and lead our ML & AI function from the ground up. This is a foundational leadership role responsible for defining our AI strategy, building a high-impact team, and delivering production-grade ML and AI capabilities embedded directly into core payer workflows. This leader will sit at the intersection of Engineering, Product, Clinical Operations, and Data — translating real-world healthcare complexity into safe, scalable AI systems that measurably improve automation, accuracy, and clinical efficiency. This is not a research role. It is a production AI leadership role focused on delivering real, regulated, enterprise-grade systems. The ideal candidate is a builder-operator , not a pure data scientist and not an AI evangelist. Leadership Profile The ideal candidate is: Strategically minded but execution-oriented Comfortable with ambiguity and building from zero Equally credible with engineers and executives Passionate about responsible AI in high-stakes domains Motivated by building long-term enterprise value and capability, not short-term experiments Team-focused Comfortable in a leadership role A frequent and effective communicator Key Responsibilities 1. AI Strategy & Architecture Define and execute a multi-year AI roadmap aligned to product evolution and regulatory constraints (CMS, Medicaid, HIPAA, NCQA). Architect scalable ML and generative AI systems embedded in software products. Evaluate and select appropriate model strategies (predictive models, RAG pipelines, fine-tuned LLMs, reasoning models, agentic workflows). 2. Build & Lead the AI Function Recruit and develop a small (4 to start), experienced team of ML engineers and applied AI specialists. Establish experimentation standards, prompt engineering practices, evaluation benchmarks, and model lifecycle processes. Create a culture of pragmatic, production-focused AI engineering. 3. Production AI Delivery Deliver end-to-end ML/AI solutions, including: Risk prediction models Intelligent document summarization RAG-based or knowledge-graph-based systems Fine-tuned LLM applications Agentic automation workflows Ensure solutions are measurable, reliable, and embedded into production workflows. 4. MLOps & Governance Establish robust MLOps pipelines (training, validation, deployment, monitoring). Implement model observability, drift detection, and performance monitoring. Partner with Security and Compliance to define responsible AI standards (fairness, bias evaluation, explainability, audit trails). Design PHI-safe AI environments and secure data pipelines. 5. Cross-Functional Leadership Partner with Product and Clinical leaders to identify high-value AI use cases. Translate complex AI concepts into executive-ready business cases. Guide organizational AI literacy and responsible adoption. Skills, Knowledge & Expertise BS in Computer Science, Data Analytics, ML/AI, or related field. Minimum 10+ years in ML/AI engineering or applied data science Minimum 4+ years leading teams or building AI functions in a Senior Manager or Director role Proven track record delivering production ML, generative AI, and/or agentic systems at scale. Demonstrated experience with relevant frameworks and patterns, e.g., Model evaluation and benchmarking LLM fine-tuning (e.g., LoRA, instruction tuning) RAG and/or KG architectures Agentic systems & workflows HuggingFace ecosystem PyTorch / TensorFlow MLOps (CI/CD for ML, monitoring, versioning) Experience deploying and running ML/AL in cloud-native environments (AWS preferred) Demonstrated experience applying governance and guardrails to AI and agentic systems Strong understanding of regulated data environments (healthcare preferred) What’s Preferred Masters of PhD in Computer Science, Data Analytics, ML/AI, or related field Agentic AI framework experience (LangGraph, CrewAI, or similar) Experience building AI platforms in PHI-safe or regulated environments Knowledge of FHIR APIs and healthcare ontologies (SNOMED, LOINC) Experience building AI Centers of Excellence or practice areas Healthcare payer domain expertise (UM, CM, risk scoring, prior authorization) Familiarity with CMS and Medicaid compliance considerations What Success Looks Like (First 18–24 Months) AI roadmap aligned to product strategy and regulatory realities Built and retained a high-performing ML/AI team Production deployment of multiple ML/AI/LLM-powered features Implemented MLOps platform and model governance framework Demonstrated measurable operational impact (e.g., reduced manual review time, improved prediction accuracy, workflow automation gains) Responsible AI framework implemented with auditability and explainability Compensation & Benefits The expected total cash compensation for this role is market-driven, with an expected starting