• Design and deliver distributed architectures for large-scale, cloud-based enterprise systems (AWS, Azure, GCP). • Integrate Agentic AI capabilities into enterprise software. • Act as a technical authority, setting standards for security, scalability, and performance, ensuring poor code or weak architecture is prevented from entering production. • Translate vague customer requirements into actionable technical designs. • Lead discussions, challenge ideas, and align teams on a clear technical direction. • Collaborate with engineers and stakeholders to ensure timely delivery of MVPs and production-ready solutions.

• Define and drive product strategy for GitLab's AI Developer Tools, with a focus on command line interface (CLI) experiences and IDE extensions that fit naturally into developers' daily workflows. • Partner closely with engineering, design, and cross-functional stakeholders to translate developer needs into clear problem statements, prioritized roadmaps, and shippable increments. • Own the roadmap and execution for the CLI as a primary deliverable, making tradeoffs that balance developer experience, product quality, and iterative delivery. • Lead discovery with real software developers (including hands-on testing of workflows) to validate usability, measure impact, and continuously improve end-to-end experiences on the command line and in the IDE. • Coordinate launches and ongoing iteration for new capabilities, aligning release readiness across product, engineering, and go-to-market partners as needed. • Stay current on the AI coding assistant landscape and developer tool expectations, using competitive and market insights to inform product decisions without losing focus on GitLab's differentiated platform approach. • Contribute as a senior individual contributor on the AI Product team, collaborating with a close-knit group of teammates and reporting to the Vice President of Product for AI.

• Architect and maintain Python-based services using FastAPI for internal and customer-facing AI use cases • Build and scale secure, well-structured API endpoints that interface with LLMs, vector stores, and agentic tools • Implement orchestration logic and tool chaining for advanced agent workflows • Optimize service performance and latency across AI infrastructure layers • Collaborate with frontend, AI, and devops teams to ensure system-wide reliability and observability • Set up robust test coverage and CI pipelines for backend services • Contribute to our modular architecture for tool-based reasoning agents • Stay current with emerging trends in AI engineering, LLM integrations, and scalable backend systems

• Lead the design, development, and ownership of AI-powered internal platforms and systems, including initiatives built from the ground up. • Own end-to-end delivery of complex projects, from problem discovery and system design through production rollout and iteration. • Design and implement AI-driven features, intelligent automation, and orchestration systems that enhance customer experiences and compound productivity across teams. • Embed AI capabilities into customer-facing products, internal tools, and operational workflows, ensuring solutions integrate naturally and deliver measurable value. • Build full-stack systems spanning backend services, APIs, data layers, and frontend interfaces. • Translate ambiguous business and operational problems into scalable, maintainable, AI-enabled technical solutions. • Apply strong systems thinking to ensure reliability, performance, security, permissions, and auditability across platforms. • Collaborate closely with engineers, teams, and stakeholders across the company to identify high-impact opportunities and drive adoption. • Participate in production support and incident response for owned systems, helping define and uphold reliability and operational best practices. • Mentor AI team members through code reviews, design feedback, and hands-on guidance, raising the overall quality bar of the team.