Lead Software Engineer

UnitedHealth Group•Washington, DC

8h•$112,700 - $193,200•Remote

About The Position

As a Lead Software Engineer within Optum Technology, you will lead the design, development, and deployment of enterprise-grade AI and machine learning solutions that transform healthcare delivery. Working with cross-functional engineering, data science, and business teams, you will architect scalable MLOps pipelines, build cloud-native data workflows, and deploy cutting-edge Generative AI and retrieval-augmented generation (RAG) capabilities. This role offers an exciting opportunity to leverage innovative AI platforms, cloud architectures, and modern software practices to solve complex healthcare challenges and drive continuous engineering excellence. You’ll enjoy the flexibility to telecommute from anywhere within the U.S. as you take on some tough challenges.

Requirements

10+ years of experience in Software Engineering building high-quality, scalable applications
5+ years of experience with distributed computing framework like Spark with PySpark or Scala and high performance computing
5+ years of data engineering principles, relational and non-relational databases (SQL, NoSQL or Vector databases)
4+ years of programming experience with Python
4+ years of experience and core software engineering best practices, including Git version control and unit testing
3+ years of experience implementing CI/CD pipelines and automated deployment practices for production software delivery
3+ years of experience with Databricks
3+ years of cloud environments (AWS, Azure, GCP) and containerization
3+ years of experience in (IaC) infrastructure as Code (Terraform or CloudFormation)
1+ years of productivity improvement using and leveraging AI tools (e.g., GitHub Copilot, Claude)
1+ years of AI/ML engineering experience, with a proven track record of designing, building, and deploying ML models in a production environment

Nice To Haves

Master's degree in Artificial Intelligence, Machine Learning, Data Science, or a related quantitative field
Proficiency in .NET for backend development and exposure to React for building responsive user interfaces
Experience working in the healthcare sector, specifically in clinical domains
Excellent communication and presentation skills, with the ability to explain complex technical AI/ML concepts to non-technical stakeholders
3+ years of experience building and automating end-to-end MLOps pipelines (e.g., using MLflow, Kubeflow, Airflow, or cloud-native tooling)
Experience with Generative AI, Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG) frameworks.
1+ years with Azure Machine Learning

Responsibilities

Lead the end-to-end design, development, deployment, and operationalization of production-grade AI-powered solutions and machine learning models to address complex business challenges with an emphasis on responsible AI use
Build, architect, and support scalable cloud-based AI/ML systems, data pipelines, and feature workflows for both batch and real-time workloads using structured and unstructured data
Establish and drive end-to-end MLOps practices, including data ingestion, feature engineering, model training, validation, automated monitoring, operational governance, and production troubleshooting
Define architectural roadmaps, reference frameworks, and standard operating procedures (SOPs), ensuring solutions align with organizational standards for reliability, fairness, transparency, security, and cost efficiency
Provide technical leadership, architectural guidance, and mentorship across cloud, AI/ML, and data engineering teams while driving engineering excellence and reducing technical debt
Establish data governance practices and quality validation processes while ensuring compliance with security and privacy standards
Partner with product management, data scientists, architects, and business stakeholders to translate strategic requirements into deliverable AI capabilities and communicate technical approaches to leadership
Use enterprise-approved AI tools and evaluate emerging trends to streamline workflows, automate tasks, inform solution design, and drive continuous improvement within the software engineering lifecycle