Senior Customer Success Architect (AI)

Overview

What you'll do

In this role, you'll be part of the Customer Success Architect (CSA) team within the Sales and Success department, focusing on the adoption of Atlassian's AI platform, Rovo. As a Senior CSA (AI), you'll lead time-boxed, co-built technical engagements that take customers from "Rovo is turned on" to real, measurable business value in production, and own the methodology that makes those engagements repeatable and scalable. You'll partner directly with enterprise teams on the most complex activations, while also building playbooks, running internal enablement, and maintaining the relationship with Rovo Product and Engineering. Your role bridges deep AI and Atlassian platform expertise with customer success leadership, focusing on measurable adoption outcomes at both the individual engagement and program level. We're hiring a Senior CSA (with an AI focus) to join our Customer Success Architect team, reporting to a regional leader. You lead through domain expertise, playbook ownership, and internal enablement, not through direct reports. Atlassian Senior CSAs are technical experts with Atlassian solutions, delivering guidance to drive value realization and adoption. They partner with Pre-Sales, Post-Sales, and Product teams to provide solutions and technical guidance to help customers achieve their desired outcomes and goals. You'll help our strongest promoters showcase their successes to their peers and serve as the tip of the spear in expanding the reach of AI and the Atlassian System of Work into new use cases and markets. As a Senior CSA (AI), you aim to help enterprise customers get the most out of their Atlassian investment. Responsibilities

On your first day, we'll expect you to have:

• Discovery. Facilitate use case discovery workshops with customer teams to identify high-impact, high-value AI opportunities. Understand their workflows, pain points, and where Rovo agents and search can deliver measurable outcomes. Lead the most complex engagements: large enterprises with multiple stakeholder groups, cross-functional AI strategies, and high-visibility use cases.
• Design. Co-build custom Rovo agents and configure third-party connectors alongside customer teams. Customers are hands-on keyboard. Design solutions tailored to their workflows using the Atlassian platform (Jira, Confluence, Rovo Studio).
• Deploy. Guide customers from sandbox to production. Validate that agents are performing, connectors are flowing data, and teams are enabled to iterate and scale independently post-engagement.
• Enable. Own the engagement methodology. Build and iterate the playbooks the AI pod follows. Onboard and train new team members on Rovo capabilities, product changes, and engagement standards.
• Bridge Product & Field. Maintain the working relationship with Rovo Product and Engineering. Synthesize field signals into structured product feedback that influences roadmap priorities.
• Collaborate. Work with Account Teams and Customer Success Managers to plan customer-specific activation journeys and hand off for sustained growth.
• Advocate. Meet with Rovo Product, Engineering, and AI teams to surface product friction, share field patterns, and improve the adoption motion.
• Travel up to 15% domestically and internationally for events and customer meetings.

Pay Ranges In The United States , we have three geographic pay zones. For this role, our current base pay ranges for new hires in each zone are: Zone A : $180,000 - $235,000 Zone B : $162,000 - $211,500 Zone C : $149,400 - $195,050 Qualifications

• Customer Success & Experience: 7-10+ years in Customer Success, AI consulting, or technical advisory roles, managing Enterprise customers with complex SaaS portfolios. Experience technically leading or mentoring a team without formal management authority.
• Technical Aptitude: Demonstrated expertise with AI platforms, the Atlassian ecosystem (Jira, Confluence, Rovo), and technical product demos. Deep experience building AI agents, prompt engineering, and configuring integrations between enterprise knowledge sources and AI systems. Hands-on experience helping organizations adopt and operationalize AI tools in production environments at scale (10,000+ users). Familiarity with LLM-powered architectures and agentic workflows.
• Solution Area Expertise: Deep expertise in enterprise workflows across product development, IT service management, or business operations. Experience designing AI-powered solutions for incident response, backlog management, knowledge discovery, and cross-team collaboration. Strong understanding of third-party connector ecosystems (Google Drive, SharePoint, Slack, GitHub, etc.) and how to integrate organizational knowledge into AI systems.
• Product Mindset & Communication: Strong product mindset with a track record of building playbooks, methodologies, or enablement programs that others adopt and scale. Comfortable owning problems end-to-end from discovery to deployment. Effective communicator who can influence executive stakeholders and translate AI capabilities into business outcomes for non-technical audiences. Delivers durable solutions but can quickly prototype when needed.