AI/ML Engineer

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.
Primary Responsibilities
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
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
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
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
Required Qualifications
Bachelor's degree in computer science, Data Science, Engineering, Mathematics, Statistics, Artificial Intelligence, or related field
5+ years of experience in Machine Learning, Artificial Intelligence, Data Science, Software Engineering, or related disciplines
Experience developing and deploying machine learning solutions in enterprise or cloud environments
Experience building machine learning pipelines and production-ready AI solutions
Experience working with APIs, cloud-based AI services, and distributed data platforms
Experience integrating AI/ML solutions into business applications and workflows
Knowledge of model monitoring, performance evaluation, and production support processes
Solid understanding of:
Machine Learning
Statistical Modeling
Predictive Analytics
Model Evaluation
Feature Engineering
Familiarity with MLOps practices including model deployment, monitoring, experiment tracking, and lifecycle management
Solid programming skills in Python and SQL
Understanding of Responsible AI, model governance, and compliance requirements
Proven solid analytical, problem-solving, communication, and collaboration skills
Preferred Qualifications
Experience deploying machine learning solutions using Azure ML, SageMaker, Vertex AI, MLflow, Kubeflow, or similar platforms
Experience with distributed data processing technologies including Spark, Databricks, PySpark, Kafka, or modern data engineering platforms
Experience developing machine learning and deep learning solutions using TensorFlow, PyTorch, or equivalent frameworks
Experience with Generative AI technologies including:
Large Language Models (LLMs)
Retrieval-Augmented Generation (RAG)
Embeddings
Semantic Search
Agentic AI concepts
Experience integrating AI services and model APIs into enterprise applications
Experience contributing to reusable AI frameworks, engineering accelerators, or platform capabilities
Experience working within healthcare, financial services, insurance, or other regulated industries
Familiarity with model monitoring, observability, and operational analytics practices
Familiarity with NLP, recommendation systems, forecasting, anomaly detection, or intelligent automation solutions
Understanding of Responsible AI, model risk management, and governance frameworks
Contributions to AI innovation initiatives, open-source projects, technical publications, or enterprise transformation efforts
At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.

Work arrangement
No