Director, AI & ML; US - Remote

Position: Director, AI & ML (US - Remote)
Director, AI & ML (US - Remote)

Department: Engineering

Employment Type: Full Time

Location: US

Reporting To: John Puopolo

Compensation: $210,000 - $275,000 / year

Description

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
• Hugging Face ecosystem
• PyTorch / Tensor Flow
• 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 (Lang Graph, 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 &…