Job Title

AI/ML EngineerKey ResponsibilitiesBuild and deploy production-grade ML, NLP, and GenAI modelsImplement modern AI architecture patterns: microservices, feature stores, real-time/batch inference, embeddings, and retrieval pipelinesConvert functional requirements into scalable AI solutionsDevelop and optimize models for claims automation, fraud detection, risk prediction, provider optimization, and document AIWork 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 KubernetesApply LLM Ops foundations for RAG and GenAI use casesWork cross-functionally with product, cloud engineering, SMEs, and clinical teamsParticipate in Agile processes and document reusable patterns and decisionsRequired Qualifications5–7 years of AI/ML engineering experienceStrong skills in Python (PyTorch/TensorFlow), NLP/LLMs/transformers, GenAI tooling, SQL, and data pipelinesExperience deploying ML/LLM systems in Azure/AWS/GCPSolid understanding of U.S. healthcare data and compliant solution design (HIPAA, RBAC, audit logging)Experience working in ML Ops / LLM Ops environmentsTechnical Skills & ExperienceExpertise in AI/ML model development and deploymentProficiency in Python programming language and various AI frameworks such as PyTorch and TensorFlowFamiliarity with Natural Language Processing (NLP), large language models (LLMs), and transformer modelsHands-on experience with cloud platforms like Azure, AWS, or GCP for deploying ML solutionsStrong knowledge of healthcare data privacy regulations and compliance standardsExperience in implementing CI/CD pipelines for machine learning workloadsPreferred QualificationsPrior experience with Optum/UHG/UHC in claims, analytics, PA/CAVA, or Document AIFamiliarity with Optum AI platforms and AI Dojo programsExposure to large-scale AI delivery models