AI or 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.

We're looking for a hands-on AI/ML Engineer to design, develop, deploy, and support machine learning solutions that drive business outcomes and intelligent decision-making. This role focuses on building scalable AI/ML capabilities, operationalizing models, and supporting the end-to-end machine learning lifecycle.

The ideal candidate possesses solid machine learning engineering fundamentals, software engineering skills, and experience working with modern AI platforms. You will partner with data scientists, senior AI engineers, data engineers, and platform teams to develop, deploy, monitor, and continuously improve AI solutions in production environments.

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
• Support MLOps practices including:
  • Experiment tracking
  • Model versioning
  • Deployment automation
  • CI/CD integration
  • Model lifecycle management
• Build automated workflows that enable reliable model deployment and operation
• 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 the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

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
• Understanding of Responsible AI, model governance, and compliance requirements
• Familiarity with MLOps practices including model deployment, monitoring, experiment tracking, and lifecycle management
• Solid programming skills in Python and SQL
• 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 NLP, recommendation systems, forecasting, anomaly detection, or intelligent automation solutions
• Familiarity with model monitoring, observability, and operational analytics practices
• Understanding of Responsible AI, model risk management, and governance frameworks
• Proven 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.

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