• Lead the design of the GCP HCP platform architecture, including multi-region scalability, multi-tenancy and isolation, automated lifecycle management, and operational resilience • Design and evolve the team's harness engineering infrastructure: the system of architectural constraints, custom linters, structural tests, CI gates, and feedback loops that enable AI agents to produce reliable work at scale • Define and maintain the team's documentation architecture — a structured knowledge base that serves as the source of truth for both agents and engineers, treating AGENTS.md as the table of contents with deep references into design documents, architecture decision records, and operational runbooks • Decompose complex system goals into well-bounded building blocks suitable for agent-driven implementation; evaluate when agent-generated approaches are sound and when they introduce unacceptable risk • Identify and address architectural drift, entropy, and emergent quality issues across a large, agent-maintained codebase — designing systematic "garbage collection" processes to fight decay • Lead architectural discussions across the HyperShift project, Cluster API communities, GCP platform integrations, and internal Red Hat teams • Establish and enforce patterns for secure, maintainable, and observable systems — defining the module boundaries, dependency hierarchies, and interface contracts that constrain the solution space for both humans and agents • Mentor senior engineers in harness engineering practices: crafting effective specifications, designing structural constraints, building agent-friendly documentation, and developing critical review skills for agent output • Define quality bars, test strategies, and operational readiness criteria for agent-produced features, collaborating with Product Management and technical support to ensure production standards are met • Serve as an escalation point for complex customer issues and production incidents beyond front-line technical support, applying deep platform knowledge to diagnosis and resolution • Participate in on-call rotations to support production managed services • Maintain a visible technical leadership presence in the Kubernetes, OpenShift, and GCP communities

• Write maintainable, testable, and efficient code for our SaaS platform. • Use modern, human-in-the-loop, and agentic AI workflows for maximum productivity. • Participate in code reviews and continuously learn best practices. • Research and implement technically feasible solutions to work items. • Work on both front-end and back-end projects as part of a cross-functional team. • Work with generative AI models to develop cutting-edge features for our customers. • Collaborate with QA to ensure the delivery of high-quality features. • Create and maintain software documentation to support our development process. • Help integrate software components into a fully functional system. • Contribute to evaluating and deploying tools, processes, and metrics. • Provide support and consultation on software systems usage. • Follow industry standards and project plans to ensure compliance and quality.

• Manage a team of architects and contribute to beneficial technologies • Identify how AI can be leveraged across the company • Mentor architects from diverse backgrounds • Drive the establishment of our architecture methodology • Identify architectural investment needs based on the product roadmap • Partner with Product, Design, and Engineering Management to execute architecture work • Communicate architectural efforts and future technology vision

• Spend the majority of your time coding and developing backend services and ML pipelines • Contribute to the development of new internal products that leverage LLMs and Agentic AI • Build and maintain the backend connections between our custom quoting engine, Salesforce, and internal data lakes • Collaborate with the team to solve synchronization challenges • Take defined product requirements and turn them into well-tested, production-ready code • Participate in code reviews and help maintain deep observability for every service the team ships.