Senior Manager, AI/ML Engineering - Remote

UnitedHealth Group•Eden Prairie, MN

9h•$148,900 - $255,300•Remote

About The Position

As a Senior Manager of AI/ML Engineering within the Chief Digital Office, Consumer Engineering team, you will lead and manage the engineering team responsible for owning the intelligence layer of our digital solutions. In this remote leadership role, you will direct the strategy, design, evaluation, and operational delivery of enterprise AI components—including LLM workflows, RAG pipelines, agentic behaviors, classifiers, copilots, and AI evaluation frameworks. You will guide engineering talent to ensure all AI capabilities are useful, measurable, safe, performant, and fit for enterprise scale. 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.

Requirements

Bachelor's degree in Computer Science, Data Science, AI, or a related technical field, OR 4+ years of equivalent software/AI engineering experience in lieu of a degree
8+ years of experience in software engineering, data science, machine learning, or AI engineering
4+ years of practical engineering experience building and deploying production AI applications (including LLMs, RAG patterns, vector search, embeddings, prompt engineering, and agent frameworks)
4+ years of strong hands-on Python engineering experience, including model APIs, orchestration frameworks, data pipelines, and evaluation tooling
3+ years of technical leadership or engineering management experience leading and developing engineering teams
3+ years of experience designing testable, governable AI workflows and applying Responsible AI practices (mitigating hallucination risks, bias/fairness concerns, and model safety risks)

Nice To Haves

Proven experience leading engineering teams in building and scaling LLM-enabled applications and copilots within enterprise settings
Demonstrated experience establishing controlled testing, evaluation frameworks, scoring rubrics, and model monitoring prior to broad rollout
Experience developing AI/ML solutions within healthcare, consumer digital platforms, or large-scale consumer applications
Proven professional background as an ML Engineer, AI Engineer, Applied Scientist, Data Scientist with engineering depth, or Forward-Deployed Engineer with AI specialization
Proven solid cross-functional collaboration skills with Responsible AI, security, legal, compliance, and enterprise architecture teams

Responsibilities

Lead, manage, and empower a high-performing engineering team responsible for designing, building, and deploying the intelligence layer for digital consumer applications
Direct the architectural strategy and technical execution for LLM workflows, prompt chains, agentic behaviors, RAG patterns, copilots, and classification/summarization systems aligned to business goals and risk profiles
Establish engineering standards, prompt strategies, tool-calling patterns, and safety guardrails to ensure AI solutions are explainable, testable, and governable across the enterprise
Drive team delivery and continuous improvement using enterprise-approved AI tools to streamline workflows, automate complex technical tasks, and optimize engineering velocity
Oversee the implementation of evaluation datasets, scoring rubrics, model-performance testing, and failure-mode analysis to continuously improve output quality, accuracy, latency, and cost efficiency
Evaluate emerging AI/ML trends, industry advancements, and novel architectures to inform solution design, technology roadmaps, and strategic innovation
Partner with Data Engineering leadership to ensure AI applications integrate with trusted, governed, and enterprise-grade data pipelines
Collaborate with cross-functional executive leadership, QA, Responsible AI, security, privacy, compliance, and architecture teams to ensure AI components fulfill enterprise compliance and risk standards
Champion production readiness, model observability, drift monitoring, and thorough technical documentation to enable steady-state supportability
Mentor, coach, and develop engineering talent, setting clear performance goals, conducting reviews, and cultivating an inclusive culture of engineering excellence