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).
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.
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.
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.
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.

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)

Director, AI & ML

Key skills