Lead AI ML Engineer Remote Nationwide or Hybrid in MN or DC

Lead AI/ML Engineer

Optum Tech is a global leader in health care innovation. Our teams develop cutting-edge solutions that help people live healthier lives and help make the health system work better for everyone. From advanced data analytics and AI to cybersecurity, we use innovative approaches to solve some of health care's most complex challenges. Your contributions here have the potential to change lives.

Ready to build the next breakthrough? Join us to start Caring. Connecting. Growing together.

As a Lead AI/ML Engineer within the Optum Health Financial Tech division at Optum Technology, you will design, develop, and scale state-of-the-art data platforms and pipelines that drive advanced analytics and machine learning. You will work on high-impact projects enabling generative AI solutions, such as RAG-style architectures, and optimize large-scale ingestion frameworks using technologies like Databricks, Spark, Snowflake, and AWS/Azure. This role offers the unique opportunity to build and scale critical data infrastructure that directly affects health outcomes, bringing cutting-edge AI capability into production.

You'll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges.

For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.

Primary Responsibilities:

• Design, develop, and deploy scalable AI-powered solutions, data pipelines, and data platforms to address complex business challenges, with an emphasis on responsible AI use
• Build and optimize ingestion frameworks for large-scale structured and unstructured data, supporting streaming and event-driven sources
• Partner with cross-functional stakeholders to understand evolving data and AI needs, defining long-term technical solutions and translating ambiguous problems into scalable roadmaps
• Enable and support machine learning and AI workflows, including feature engineering, data preparation, and model deployment support
• Drive strategic initiatives around Generative AI, including RAG-style architectures, data quality, observability, lineage, and governance
• Evaluate emerging trends in AI and machine learning to inform solution design, strategic innovation, and the rapid experimentation of AI/ML solutions
• Introduce and evolve best practices in data modeling, orchestration, testing, and monitoring, while establishing reusable parsing, enrichment, analytic, and service libraries
• Identify and champion opportunities for platform scalability, performance optimization, cost efficiency, and continuous improvement
• Leverage enterprise-approved AI tools to streamline workflows, automate tasks, and accelerate team delivery

You'll 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 provide development for other roles you may be interested in.

Required Qualifications:

• Bachelor's degree or equivalent experience (e.g., 4+ years of additional software engineering experience)
• 5+ years of experience designing, building, and operating production-grade data pipelines and platforms across batch and streaming workloads
• 5+ years of hands-on software development experience using Python and/or Java, including code reviews, packaging, and deployment
• 5+ years of experience with distributed data processing systems like Spark (PySpark) and cloud-based platforms such as Databricks
• 2+ years of experience leveraging and deploying Generative AI and/or LLM use cases to production environments (such as RAG architectures)
• 3+ years of experience building ingestion frameworks for structured/unstructured data and scaling ETL/ELT pipelines using orchestration tools like Airflow
• 3+ years of experience with data lakes/warehouses (e.g., Databricks, Snowflake) and advanced SQL querying/tuning
• 2+ years of experience with Dev Ops practices, CI/CD tools (e.g., Git Hub Actions), containerization (e.g., Docker, Kubernetes), and Infrastructure-as-Code (e.g., Terraform)

Preferred Qualifications:

• Experience with major cloud platforms (AWS, Azure, and/or GCP), including managed data services
• Experience with streaming and event-driven architectures (e.g., Kafka, Kinesis, Event Hubs)
• Experience with data quality and validation frameworks (e.g., Great Expectations, Deequ) and data observability tooling
• Experience enabling MLOps practices, including feature stores, model registries, experiment tracking, and deployment automation
• Experience with lakehouse architectures, Delta Lake, and advanced Spark optimization/performance tuning
• Experience with machine learning and predictive analytics
• Familiarity with security and privacy concepts for data platforms (e.g., least privilege, PII/PHI handling) and working with compliance partners
• Proven ability to lead through influence, mentor junior engineers, and translate ambiguous problems into scalable technical roadmaps

All employees working remotely will be…