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
- Design, build, deploy, and maintain scalable machine learning and Generative AI solutions supporting clinical, pharmacy, payer, and operational use cases
- Develop predictive models, recommendation systems, NLP solutions, deep learning models, and LLM-based applications to solve complex business problems
- Build and optimize data pipelines, feature engineering workflows, and model training frameworks using large-scale healthcare and business datasets
- Design and implement Retrieval-Augmented Generation (RAG) architectures, vector search solutions, prompt engineering techniques, and LLM evaluation frameworks
- Fine-tune and deploy foundation models while ensuring reliability, scalability, security, and performance
- Implement MLOps and LLMOps best practices including CI/CD pipelines, automated testing, model deployment, experiment tracking, monitoring, and observability
- Collaborate with data scientists, product managers, architects, and business stakeholders to translate requirements into scalable AI solutions
- Ensure adherence to Responsible AI principles, data governance standards, HIPAA requirements, and enterprise security controls
- Analyze model performance and continuously improve model accuracy, latency, cost, and user experience through experimentation and optimization
- Provide technical leadership, mentor junior engineers, conduct code reviews, and contribute to engineering best practices and standards
- Evaluate emerging AI technologies and recommend innovative approaches that create measurable business value
- Support production AI systems through monitoring, issue resolution, root cause analysis, and continuous improvement efforts
- 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, Engineering, Data Science, Mathematics, or a related technical field
- 5+ years of professional software engineering, machine learning engineering, or AI development experience
- 3+ years of experience building and deploying machine learning solutions in production environments
- Hands-on experience with Generative AI technologies including Large Language Models (LLMs), embeddings, prompt engineering, RAG, and vector databases
- Experience developing cloud-native solutions utilizing AWS, Azure, or Google Cloud Platform
- Experience with containerization and orchestration technologies including Docker and Kubernetes
- Knowledge of MLOps practices including model deployment, monitoring, experiment tracking, and CI/CD automation
- Solid understanding of machine learning fundamentals including supervised learning, unsupervised learning, statistical modeling, and model evaluation
- Solid proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, Scikit-Learn, or equivalent
- Solid expertise in SQL, data modeling, and large-scale data processing technologies
- Proven ability to communicate complex technical concepts to both technical and non-technical audiences
Preferred Qualifications
- Master's degree or higher in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related discipline
- Healthcare industry experience including claims, pharmacy, clinical, provider, payer, or consumer health domains
- Experience with healthcare interoperability standards such as HL7, FHIR, or EHR integrations
- Experience with Databricks, Snowflake, Apache Spark, Kafka, or lakehouse architectures
- Experience with MLflow, SageMaker, Azure ML, Vertex AI, or similar AI platform technologies
- Experience implementing AI governance, model risk management, and Responsible AI frameworks
- Knowledge of agentic AI architectures, multi-agent systems, workflow orchestration, and enterprise AI assistants
- Knowledge of vector databases such as Pinecone, FAISS, Weaviate, or pgvector
- Understanding of healthcare privacy, compliance, and security frameworks including HIPAA and HITRUST
- Familiarity with LangChain, LlamaIndex, Semantic Kernel, AutoGen, or equivalent AI development frameworks
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.