AI Project Manager / AI Tools Program Manager

Role Overview
 

We are looking for an AI Project Manager to own the adoption, operation, and continuous improvement of AI tools across the company.
This role sits at the intersection of AI tooling, internal product management, engineering operations, and team enablement. You will observe how teams use AI in their daily work, identify gaps, coordinate improvements, onboard users, and manage the internal team responsible for developing and maintaining our AI tooling ecosystem.

The ideal candidate has hands-on understanding of modern agentic AI tools and workflows — such as Claude Code, Cowork, OpenClaw, Hermes, multi-agent development pipelines, AI-assisted coding, internal automation, and AI workflow orchestration.
 

Key Responsibilities

AI Tool Adoption & Usage Oversight

• Observe how AI tools are used across teams and departments.
• Identify practical use cases where AI can improve productivity, quality, delivery speed, or operational consistency.
• Monitor adoption, usage patterns, blockers, risks, and recurring issues.
• Collect feedback from users and translate it into actionable improvements.
• Define best practices, usage guidelines, and internal standards for AI-assisted work.
• Ensure AI tools are used effectively, safely, and consistently across the company.

AI Tools Access, Maintenance & Operations

• Manage access to internal and external AI tools for company team members.
• Coordinate onboarding, permissions, accounts, keys, roles, and access policies.
• Oversee day-to-day maintenance of AI tooling together with the internal technical team.
• Track incidents, bugs, tool limitations, and operational risks.
• Ensure AI tools remain reliable, well-documented, and aligned with company needs.
• Coordinate improvements to integrations, automation flows, and internal AI infrastructure.

Team Onboarding & Enablement

• Onboard developers, product managers, analysts, QA engineers, and other team members on AI tools.
• Prepare internal documentation, training materials, demos, and usage guides.
• Run workshops and practical enablement sessions.
• Help teams understand when and how to use AI tools effectively.
• Support users in moving from ad hoc AI usage to structured, repeatable AI-assisted workflows.

Internal AI Tools Development Management

• Manage the internal team responsible for developing, integrating, and maintaining AI tools.
• Gather requirements from business, product, engineering, QA, and operations teams.
• Prioritize the AI tools backlog based on business value, user pain points, and technical feasibility.
• Coordinate development of internal AI platforms, agents, orchestration flows, connectors, dashboards, and automation tools.
• Ensure the team delivers practical, production-ready solutions rather than experimental demos.
• Track progress, remove blockers, and maintain clear communication with stakeholders.

AI Workflow & Agentic Systems Development

• Help design and improve workflows involving AI agents, coding assistants, review agents, automation agents, and internal orchestration tools.
• Define structured processes for AI-assisted analysis, development, QA, documentation, and operations.
• Support the creation of workflows where AI agents can operate safely within defined permissions, context, and review boundaries.
• Work with engineering teams to improve visibility, logging, auditability, and reliability of AI-agent interactions.
• Promote reusable patterns, shared knowledge bases, and company-wide AI standards.
   

Required Skills & Experience
 

• Strong understanding of modern AI tools, especially agentic AI systems and AI-assisted software development workflows.
• Practical familiarity with tools and concepts such as:
  • Claude Code
  • Cowork
  • OpenClaw
  • Hermes-style automation
  • AI coding agents
  • multi-agent workflows
  • MCP / tool integrations
  • workflow orchestration
  • internal AI platforms and automation systems
• Experience managing technical projects, internal tools, platform teams, or developer productivity initiatives.
• Ability to translate business and team needs into clear technical requirements.
• Strong product-management mindset: discovery, prioritization, feedback loops, roadmap ownership, and delivery tracking.
• Understanding of software development workflows, QA processes, CI/CD, issue tracking, and engineering team operations.
• Ability to work closely with developers, product managers, QA, business stakeholders, and leadership.
• Strong documentation, communication, and onboarding skills.
• Ability to distinguish between AI hype and practical, measurable business value.
   

Nice to Have
 

• Experience building or managing internal developer platforms.
• Experience with AI governance, access control, logging, auditing, or security policies.
• Familiarity with Jira, GitHub/GitLab, Slack, Google Workspace, Sentry, PostHog, or similar tools.
• Experience with prompt engineering, AI evaluation, agent monitoring, or model/tool benchmarking.
• Understanding of cloud environments, sandboxed development environments, or internal automation infrastructure.
• Previous experience introducing AI tools into engineering or operational teams.

What Success Looks Like

• Teams understand how to use AI tools correctly and confidently.
• AI tooling adoption grows in a structured and measurable way.
• Internal AI tools become reliable, documented, and actively used.
• Repetitive work is reduced through practical automation.
• AI-assisted development, QA, analysis, and operations become part of normal team workflows.
• The company has clear visibility into AI tool usage, value, risks, and improvement opportunities.
• The internal AI tools team has a clear roadmap, priorities, and delivery process.
   

Candidate Profile
 

You are not just a traditional project manager. You are a technically curious operator who understands how AI agents are changing software development and business operations.

You know that real AI transformation is not about giving everyone access to a chatbot. It is about building reliable workflows, maintaining context, setting boundaries, measuring results, and helping teams adopt new ways of working without losing control.

You should be comfortable speaking with engineers about tools, integrations, and automation, while also being able to explain AI initiatives clearly to non-technical stakeholders.

Suggested Job Title Options

• AI Project Manager
• AI Tools Program Manager
• AI Transformation Project Manager
• AI Platform Project Manager
• Internal AI Tools Manager
• AI Enablement Manager