AI/ML Engineer
Key Responsibilities
- Build and deploy production-grade ML, NLP, and GenAI models
- Implement modern AI architecture patterns: microservices, feature stores, real-time/batch inference, embeddings, and retrieval pipelines
- Convert functional requirements into scalable AI solutions
- Develop and optimize models for claims automation, fraud detection, risk prediction, provider optimization, and document AI
- Work with data engineering teams to integrate models with healthcare datasets (claims, EHR, clinical notes, PA/UM workflows)
- Implement CI/CD for ML/LLM workloads using MLflow, Vertex, SageMaker, Databricks, Airflow, Argo, and Kubernetes
- Apply LLM Ops foundations for RAG and GenAI use cases
- Work cross-functionally with product, cloud engineering, SMEs, and clinical teams
- Participate in Agile processes and document reusable patterns and decisions
Required Qualifications
- 57 years of AI/ML engineering experience
- Strong skills in Python (PyTorch/TensorFlow), NLP/LLMs/transformers, GenAI tooling, SQL, and data pipelines
- Experience deploying ML/LLM systems in Azure/AWS/GCP
- Solid understanding of U.S. healthcare data and compliant solution design (HIPAA, RBAC, audit logging)
- Experience working in ML Ops / LLM Ops environments
Technical Skills & Experience
- Expertise in AI/ML model development and deployment
- Proficiency in Python programming language and various AI frameworks such as PyTorch and TensorFlow
- Familiarity with Natural Language Processing (NLP), large language models (LLMs), and transformer models
- Hands-on experience with cloud platforms like Azure, AWS, or GCP for deploying ML solutions
- Strong knowledge of healthcare data privacy regulations and compliance standards
- Experience in implementing CI/CD pipelines for machine learning workloads
Preferred Qualifications
- Prior experience with Optum/UHG/UHC in claims, analytics, PA/CAVA, or Document AI
- Familiarity with Optum AI platforms and AI Dojo programs
- Exposure to large-scale AI delivery models
