Sr. Deployment Manager, AI & Product Office - Boston, MA or Remote

UnitedHealth Group•Boston, MA

22h•$112,700 - $193,200•Remote

About The Position

The Sr. Deployment Manager, AI & Product Office helps get the office’s AI-enabled products into use and keeps them improving. The role supports the Chief AI & Product Officer’s enterprise AI-enabled product strategy, Agenix solutions portfolio, and product capability agenda. It coordinates deployment into local teams, trains product users, and runs the post-launch cycle that turns user feedback into measured improvements. The position is accountable for adoption and measurable enterprise value: whether products are used, continue to improve, and can be deployed to subsequent markets more efficiently. The role joins a newly forming, small cross-functional product team spanning product management, engineering, product design, service design, deployment, and clinical work. The team partners with local market care team members to identify problems, design and build solutions, deploy them, and measure results, documenting lessons from each market for future deployments. The role may be performed remotely from anywhere in the U.S. Employees in the Minneapolis or Washington, D.C. area must work in the office at least four days per week. Remote employees must comply with UnitedHealth Group’s Telecommuter Policy.

Requirements

At least 7 years of experience deploying technology or launching new sites, services, or markets in healthcare delivery settings.
Experience in the healthcare industry and/or within a large, complex, matrixed organization.
Experience leading a new technology deployment or new site or market launch, with the ability to describe adoption and outcomes after launch.
Experience designing and delivering training for frontline clinical or operational staff.
Uses AI tools in their work and has built and operated an agent, automation, or large language model tool, at work or as a personal project.
Ability to work at both enterprise and individual-market levels and determine which level should own a decision.
Business acumen, including tracking and analyzing adoption and outcome measures using spreadsheets, SQL, or similar tools.
Strong writing skills; decisions, findings, and disagreements are documented in writing.
Ability to build relationships and earn trust with frontline staff, care team members, and market leaders at all organizational levels.
Working proficiency with GitHub and AI coding and agent tools.
Ability to work as a senior individual contributor, owning complex problems end to end and delivering across functions.

Nice To Haves

Electronic health record implementation or training credentials, such as Epic.
Change management training or certification.
Experience deploying across multiple sites or markets and adapting to local differences.
Experience supporting an AI or machine learning product after launch.
Clinical or practice background, such as nursing, medical assistant, or practice management.

Responsibilities

Plan and run deployments into local teams, including readiness, sequencing across sites, go-live support, and decisions or support needed from market leaders.
Coordinate with engineers on releases so products are ready for users and users understand what has changed.
Work with the service designer to ensure the designed service is what goes live.
Develop and execute change management and communications plans so affected people understand the deployment’s purpose, impact, readiness requirements, and expected value.
Partner with finance, technology, data, security, legal, privacy, compliance, care teams, and other stakeholders to make deployments responsible, secure, and enterprise-ready.
Run the post-launch improvement cycle by capturing user observations, triaging feedback, routing it to the appropriate team member, and measuring whether fixes worked.
Track product adoption and outcome measures, and escalate when a product is not being used or an agent’s performance is drifting.
Close the feedback loop with product users by communicating what changed in response to their reports.
Document weekly learnings for review on cadence and conversion into decisions.
Design and deliver training for care teams, Sr, and managers, tailored to their work and generated with AI tools where efficient.
Build a network of local champions in each market to train others and bring feedback back to the team.
Help market teams improve AI fluency and use and build with AI tools safely through a supported path, including templates, evaluation bars, and a way to ship.
Create a deployment and training playbook from the first market so subsequent markets can use documented methods.
Determine which deployment components are market-specific and which are enterprise-wide, and document the playbook accordingly.
Own difficult problems end to end as a senior individual contributor, working across functions to deliver them.