Head of AI-Native Product Operations

Lansweeper seeks a senior leader to design and operate AI native product workflows, establishing a shared AI and data foundation that connects product, engineering, and go-to-market teams. This greenfield, hands-on role emphasizes building, integrating, and scaling AI driven processes across the product organization.

Responsibilities

• Audit and map the product organization's toolchain, workflows, and friction points across product management, product marketing, UX, enablement, and the partner ecosystem. Prioritize high impact opportunities for AI native automation.
• Design and implement AI powered workflows leveraging orchestration tools such as n8n or Make, along with API integrations and AI agents to automate coordination, reporting, and documentation; manage the full lifecycle from prototype to production.
• Partner with Engineering to streamline the product engineering interface, including planning handoffs, sprint coordination, release management, QA feedback loops, and cross functional reporting. This area represents the strongest current adoption with the greatest near-term demand.
• Create AI native workflows linking Product and the GTM organization to ensure smooth flow of product context, competitive intelligence, and launch information across the boundary.
• Own and configure product owned tools such as Enterpret and maintain a centralized product knowledge layer that makes strategy, OKRs, architecture, personas, and competitive intelligence retrievable by AI agents and team members alike.
• Connect the product toolchain into automated workflows via APIs and MCP, linking product planning, project management, analytics, design, and communication tools into a cohesive operating layer.
• Develop and iterate on AI agents for product operations tasks, including intake triage, PRD generation, status reporting, stakeholder perspective simulation, competitive analysis, and meeting preparation.
• Enable the product organization to adopt AI native workflows by designing onboarding experiences, running workshops, establishing guardrails and documentation, and building fluency across all product functions.
• Measure and report on operational impact such as hours saved, cycle time, decision quality, and adoption rates, and build dashboards that make the value of AI native operations visible.
• Collaborate with Lansweeper's Operations and IT teams to ensure product workflows and tooling sit on the shared company wide AI and data foundation, aligning on security, governance, and enterprise standards.
• Monitor the evolving AI tooling landscape, evaluate new platforms and models, and keep Lansweeper's product operations infrastructure at the frontier.

Requirements

• 5+ years in product operations, technical program management, solutions architecture, or a similar role in B2B SaaS.
• A demonstrable track record of building AI powered workflows or automation systems in a professional context, not just using AI tools but designing, connecting, and maintaining multi-step automated workflows.
• Hands-on experience with AI agent building, orchestration platforms (n8n, Make, Zapier), API integrations, MCP, vector databases, or similar technical infrastructure.
• Deep familiarity with the product organization toolchain such as Jira, Confluence, Pendo, Enterpret, or similar platforms.
• Strong understanding of the B2B SaaS product development lifecycle across discovery, design, development, delivery, and go-to-market.
• Experience collaborating across Engineering and GTM functions on process integration, tooling, and cross functional workflows.
• A portfolio, GitHub repository, or other evidence of systems you have built. Demonstrated building ability is valued over certifications.
• Excellent English communication skills (CEFR C1+). Ability to write documentation, present to leadership, and facilitate workshops with equal confidence.

Technologies

• n8n
• Make
• Zapier
• MCP (Model Context Protocol)
• Enterpret
• Jira
• Confluence
• Pendo
• Claude Cowork
• Atlassian Rovo
• vector databases

Context & Impact

For 21 years, Lansweeper has moved quickly and reinvented itself as markets, products, and customers evolve. The current turning point is the AI era, with engineering adopting AI assisted code development and QA, while product teams routinely use tools like Claude Cowork and Atlassian Rovo. The missing link is a connective layer that unifies these efforts into scalable, repeatable workflows across the organization.

Challenge

• Greenfield mandate with no predecessor, toolkit, or established playbook. The role defines the discipline as it is built.
• Structural change rather than evangelization. AI is already embraced, but rapid, coordinated change across multiple functions is required to achieve higher economies of scale and prevent drift.
• Two collaboration fronts requiring close partnership: Engineering, which has the most immediate need for process integration, and the interface between Product and GTM that demands sharper automation.
• Orchestrating complex toolchains so that agent-driven workflows span product planning, engineering, analytics, design, and communication platforms into reliable end-to-end systems.
• Defining success metrics in a field where formal benchmarks do not yet exist.

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