Senior 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 diversity and inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health equity on a global scale. Join us to start Caring. Connecting. Growing together.

As an AI/ML Engineer, you will help build and run the machine learning and generative AI capabilities behind new products that serve U.S. healthcare. You will work hands-on across the full lifecycle, from data preparation and feature engineering through model and prompt development, evaluation, deployment, and monitoring.

Careers with Optum offer flexible work arrangements and individuals who live and work in the Republic of Ireland will have the opportunity to split their monthly work hours between our Dublin or Letterkenny office and telecommuting from a home-based office in a hybrid work model.

What you will do:

Design, build, and deploy generative AI solutions - including retrieval-augmented generation, prompt and context engineering, orchestration, and agentic workflows - grounded in enterprise healthcare data

Develop and productionize classical machine learning models, covering feature engineering, training, tuning, validation, and inference

Build the evaluation harnesses, test sets, and quality metrics used to measure model and GenAI system performance, including accuracy, groundedness, latency, and cost

Implement MLOps practices such as experiment tracking, model registries, automated pipelines, CI/CD, and monitoring for drift, quality, and reliability in production

Work with data engineering to shape the datasets, semantic layers, and retrieval indexes that AI and ML workloads depend on

Partner with product, business, architecture, security, and governance stakeholders to turn use cases into workable solutions, and to explain what the models can and cannot do

Apply responsible AI practices, including privacy, safety, bias assessment, human-in-the-loop review, and traceability for sensitive and regulated data

Contribute to team engineering standards through code review, documentation, and reusable components, and share knowledge with peers

You will be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role, as well as providing development for other roles you may be interested in.

What you will bring:

Bachelor's degree in a relevant field, or equivalent professional experience

Proven experience building and deploying machine learning or AI solutions into production within public cloud environments

Proven hands-on experience developing Generative AI applications using large language models, including prompt engineering, retrieval-augmented generation (RAG), embeddings, vector search, and orchestration frameworks

Demonstrated solid understanding of classical machine learning, including feature engineering, model selection, training, tuning, and validation

Demonstrated advanced proficiency in Python and SQL, with experience using machine learning and data libraries

Proven experience with MLOps tools and practices, including experiment tracking, model versioning, automated deployment pipelines, and production monitoring

Demonstrated solid understanding of software engineering practices, including automated testing, version control, CI/CD, and production support

Demonstrated working knowledge of data preparation and data pipeline concepts, with experience working with large datasets

Demonstrated understanding of cloud security, identity and access management, and privacy principles for sensitive data

Proven ability to collaborate with technical and non-technical stakeholders and communicate AI and machine learning concepts, trade-offs, and limitations effectively

Other useful skills and experience include:

Proven hands-on experience with Azure AI services, Databricks, and Snowflake

Proven experience working with MLflow, Apache Spark, and deep learning frameworks such as PyTorch or TensorFlow

Proven experience evaluating machine learning and Generative AI system quality using offline and online methodologies and leveraging findings to drive continuous improvement

Proven experience working with vector databases, hybrid search solutions, and retrieval optimization techniques

Proven experience building agentic AI, tool-using AI systems, or conversational analytics solutions leveraging enterprise data

Proven experience with model fine-tuning, model adaptation techniques, and assessing their applicability to business requirements

Proven experience working with containerization, infrastructure as code, and technologies such as Docker, Kubernetes, or Terraform

Proven experience applying responsible AI, governance, and security controls to sensitive or regulated data environments

Proven experience within the healthcare industry

What We Offer:

Opportunities for professional development

Inclusive and supportive team culture

Key benefits: Private health insurance, wellness programs, matching pension contribution, lunch provided by the company, training opportunities, employee donations matching and others

Please note you must currently be eligible to work and remain indefinitely without any restrictions in the country to which you are making an application. Proof will be required to support your application.

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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