AI / ML Engineer
unitedhealthgroup
Job Description
- Machine Learning Development
- Design, develop, train, evaluate, and deploy machine learning models supporting:
- Predictive analytics
- Forecasting
- Recommendation systems
- Classification and regression
- Anomaly detection
- Translate business requirements into scalable AI/ML solutions
- Apply machine learning, statistical modeling, and data science techniques to solve business problems
- Perform exploratory data analysis (EDA), feature engineering, data preparation, and model experimentation
- Work with structured, semi-structured, and unstructured datasets
- Design, develop, train, evaluate, and deploy machine learning models supporting:
- AI/ML Engineering & Model Lifecycle
- Build and maintain machine learning pipelines supporting:
- Data ingestion
- Feature engineering
- Model training
- Model validation
- Model deployment
- Monitoring and retraining
- Implement model evaluation, benchmarking, and performance measurement processes
- Support model optimization and hyperparameter tuning activities
- Contribute to repeatable and scalable AI engineering practices
- Build and maintain machine learning pipelines supporting:
- MLOps & Production Deployment
- Deploy machine learning models using APIs,
containerized services, and cloud-native platforms - Contribute to reusable AI components, frameworks, and engineering assets
- Monitoring & Operational Excellence
- Monitor deployed models for:
- Accuracy
- Drift
- Latency
- Reliability
- Operational health
- Support implementation of observability capabilities including monitoring, logging, alerting, and performance reporting
- Participate in troubleshooting, root cause analysis, and production support activities
- Help ensure AI solutions meet enterprise standards for reliability and operational excellence
- Deploy machine learning models using APIs,
- Data Engineering & AI Integration
- Collaborate with data engineering teams to develop scalable data pipelines and feature engineering workflows
- Integrate AI and machine learning capabilities into enterprise applications, APIs, and business processes
- Support development of reusable features and AI services for enterprise consumption
- Responsible AI & Governance
- Follow Responsible AI practices related to explainability, fairness, transparency, and governance
- Support model validation, auditability, and compliance activities
- Adhere to organizational security, privacy, and governance standards
- Emerging AI Technologies
- Explore emerging AI, Generative AI, and Agentic AI technologies and contribute to innovation initiatives
- Support implementation of AI capabilities including:
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Embeddings
- Semantic Search
- Contribute to engineering best practices and continuous improvement initiatives
- 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