Sr. Product Manager, Enterprise Productivity & AI

Rivian•Riverdale, GA

About The Position

An individual contributor role and the front door to Enterprise Productivity & AI. You bring in demand from across Rivian, translate it into a clear problem worth solving, and shape where our roductivity, collaboration, and AI investments go next. Success depends on deeply understanding the business — how engineering, manufacturing, and corporate teams actually work — so recommendations land as real solutions rather than more tooling. You set direction and define requirements; platform administration, licensing, and renewals sit with other teams. Focus areas: Atlassian (Jira, JSM, Confluence, Rovo, Marketplace plug-ins), AI frameworks and productivity agents (Claude Cowork, Claude Code, AWS Bedrock, Gemini, NotebookLM, Glean, Devin, Windsurf), and collaboration platforms (Slack, Google Workspace, Figma, Adobe, and others).

Requirements

Bachelor's degree in IT, Business, Computer Science, or related field — or equivalent experience.
5+ years in product management, business analysis, digital workplace, or a related function.
Track record of building stakeholder trust and translating messy needs into clear requirements.
Deep hands-on Jira and Confluence experience in a working engineering environment workflows, schemes, permissions, JQL, automation, and the Marketplace ecosystem and able to hold a technical conversation with Atlassian admins and developers.
Demonstrable daily use of AI assistants and productivity agents, with a point of view on where they help and where they fail.
Working knowledge of agile practices and a structured methodology such as 5D Framework.
Comfort operating independently across competing priorities.
Strong communication skills.

Nice To Haves

Atlassian at scale — Cloud migration, plug-in rationalization, or portfolio planning (Jira Align or similar).
Supporting engineering or technical organizations as a business partner.
Hands-on Rovo Agents, Claude, or another enterprise search and knowledge-assistant platform.
Coding agents and AI-assisted development environments in practice.
AWS Bedrock or Google AI tooling in an enterprise context.
Light scripting or low-code (Python, JavaScript, Jira automation) sufficient to prototype and talk to developers.
Familiarity with IT security, data privacy, or compliance frameworks for workplace and AI tools.

Responsibilities

Primary contact for business teams bringing productivity and tooling needs to the department; own intake, triage, and routing.
Build relationships across functions and firsthand fluency in their workflows and constraints.
Reframe requests into defined problem statements with clear outcomes; say no or not yet when warranted.
Shape the portfolio roadmap — where to invest, consolidate, or sunset — backed by usage data, business impact, and total cost.
Lead build-vs-buy-vs-configure decisions and document the trade-offs.
Track the AI and workplace tooling landscape; advise leadership on what matters and what is noise.
Align Rivian’s engineering goals with the Atlassian platform.
Standardize apps to optimize spend, reduce risk, and eliminate redundancy.
Govern Confluence structure, templates, and knowledge access.
Drive Atlassian Rovo adoption across engineering workflows.
Lead integration and consolidation strategy with technical partners.
Solve root delivery and process problems instead of over-customizing software.
Lead enterprise AI assistant and agent evaluation, rollout, and adoption strategy.
Direct AI extension frameworks, including RAG, context management, and MCP integrations.
Run hypothesis-driven pilots to measure time savings, output quality, and active usage.
Partner with Security, Legal, and Privacy to establish guardrails and enablement programs.
Align with engineering leaders to integrate coding agents into core developer workflows.
Lead projects end-to-end using structured delivery frameworks to convert ambiguous needs into measured outcomes.
Drive agile execution, maintaining responsive planning, backlog refinement, and review cadences.
Partner with engineering teams to build custom tools, defining user stories, technical specs, and acceptance criteria.
Manage end-to-end delivery mechanics, including timelines, dependencies, stakeholder alignment, UAT, and rollout.