Lead AI/ML Engineer
unitedhealthgroup
Job Description
- *AI/ML and Generative AI Engineering
- Design, develop, evaluate, and deploy production-grade machine learning and generative AI solutions for PBM and RxMax use cases
- Build retrieval-augmented generation solutions, enterprise search experiences, AI agents, and repository-aware engineering assistants using approved models and platforms
- Create model and prompt evaluation frameworks covering accuracy, groundedness, safety, performance, reliability, and business value
- Operationalize models and AI services through repeatable MLOps practices, monitoring, versioning, testing, and controlled release processes
- Partner with product, engineering, operations, security, and compliance stakeholders to identify high-value use cases and translate them into measurable solutions
- *Data Engineering and Platform Development
- Architect and implement scalable ingestion, transformation, orchestration, and serving pipelines across PBM data domains
- Build curated data products and reusable data services using Azure, Databricks, ADLS, Spark/PySpark, SQL, Delta Lake, and related technologies
- Engineer data pipelines that support batch, near-real-time, analytical, operational, and AI workloads while meeting performance and reliability requirements
- Implement data quality controls, reconciliation, metadata, lineage, observability, retention, and recoverability throughout the data lifecycle
- Optimize data layout, partitioning, query patterns, compute usage, and storage to improve performance and cost efficiency at large scale
- Develop secure access patterns for sensitive healthcare and PBM information, including appropriate masking, least-privilege access, and auditability
- *Modernization and Engineering Excellence
- Apply AI-assisted, repository-aware, and specification-driven engineering practices to accelerate modernization while preserving embedded RxMax business rules
- Create reusable frameworks for documentation, code analysis, data mapping, test generation, knowledge capture, and technical onboarding
- Build automated CI/CD pipelines with unit, integration, data-quality, security, and regression controls embedded early in the delivery lifecycle
- Collaborate with software engineering, quality engineering, SRE, DevSecOps, architecture, and data teams to deliver integrated end-to-end solutions
- Contribute to architecture decisions and proofs of concept by defining success criteria, implementing representative workloads, documenting evidence, and communicating tradeoffs
- Mentor engineers and establish practical standards, reference implementations, and reusable patterns for AI and data engineering
- Comply with all applicable Company policies, procedures, and business directives, changes including those relating to work location, team assignments, work schedules, and flexible work arrangements