AI/ML

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

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  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