- Own data problem formulation for AI systems using healthcare, claims, coding, and payer data.

- Design training data strategies, including data extraction, labeling, annotation, and quality improvement.

- Mine large, complex datasets to identify high-value patterns for improving AI/coding accuracy.

- Architect and evolve AI/ML platforms covering training, batch processing, and real-time inference.

- Build multi-modal AI systems using clinical text, structured EHR data, and domain knowledge.

- Evaluate AI tools/platforms and drive Build vs. Buy decisions.

- Mentor AI engineers across NLP, Traditional ML, and GenAI.

- Collaborate with Data Engineering, GenAI/NLP, and Product teams on data and model strategy.

Must-Have :

- 8+ years building and deploying production AI/ML systems.

- Strong experience with complex, domain-specific data and high-cardinality datasets.

- Deep expertise across NLP, Traditional ML, and GenAI, with production experience in each.

- Expert-level PyTorch or TensorFlow with experience in model training at scale.

- Strong hands-on experience with data quality, labeling, and training-data curation.

- Experience with AWS/Azure/GCP and containerized AI workloads using Docker/Kubernetes.