Lead AI/ML Engineer

UnitedHealth Group•Minnetonka, MN

1d•$145,500 - $249,500•Remote

About The Position

As a Lead AI/ML Engineer within Optum Technology, you will define and execute the AI/ML engineering roadmap aligned with key business objectives. You will provide technical leadership for Machine Learning, Generative AI, Agentic AI, NLP, and Predictive Analytics initiatives, leading architectural reviews and technology selection decisions. Working alongside a collaborative team of engineers and data scientists, you will design, build, and deploy production-ready AI systems, establish end-to-end MLOps pipelines, and champion responsible AI practices to deliver scalable solutions that transform healthcare technology. You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges.

Requirements

5+ years of software engineering or data engineering experience
2+ years of hands-on Machine Learning engineering experience
Experience building and deploying production-ready ML/DL models into live environments
Experience designing or implementing cloud-native infrastructure or MLOps pipelines (e.g., Azure or GCP)
Demonstrated track record of delivering AI solutions that drive measurable business impact

Nice To Haves

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, Mathematics, or a related field
Experience delivering Large Language Model (LLM) applications, Retrieval-Augmented Generation (RAG) architectures, and AI agents
Hands-on experience with responsible AI frameworks, explainability tools, bias mitigation, and model governance
Proven solid leadership and communication skills with experience presenting technical concepts to executive stakeholders

Responsibilities

Define and execute the AI/ML engineering roadmap aligned with business objectives
Provide technical leadership for AI, Machine Learning, Generative AI, Agentic AI, NLP, and Predictive Analytics initiatives
Lead AI solution architecture reviews and technology selection decisions while mentoring and developing AI/ML engineers and data scientists
Design, build, train, evaluate, and deploy production-ready machine learning and deep learning models
Lead implementation of LLM-based applications, RAG architectures, AI agents, and conversational AI platforms
Establish model performance benchmarks, validation frameworks, responsible AI, explainability, bias mitigation, and model governance practices
Design and implement end-to-end MLOps pipelines, CI/CD standards, model monitoring, drift detection, and observability on cloud platforms (e.g., Azure or GCP)
Collaborate with data engineering teams to develop reliable data pipelines and feature stores while ensuring compliance with security and regulatory standards
Partner with business leaders and domain experts to translate complex business problems into scalable AI solutions and communicate technical outcomes to executive audiences
Strategically leverage enterprise AI capabilities to streamline workflows, optimize inference performance, solve complex challenges, and drive continuous engineering excellence