About the role
Responsibilities:
- Owned dataproblem formulation for AI systems using healthcare, claims, coding, and payer data.
- Designed training data strategies, including data extraction, labelling, annotation, and quality improvement.
- Mine large, complex datasets to identify high-value patterns for improving AI/coding accuracy.
- Architect and evolveAI/ML platformscovering 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.
Requirements:
- 8+ years building and deploying production AI/ML systems.
- Strong experience withcomplex, 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, labelling, and training-data curation.
- Experience with AWS/Azure/GCP and containerised AI workloads using Docker/Kubernetes.