• Contribute to the development and evolution of our internal platform-as-a-service used by application and service developers • Build and maintain tooling that improves developer workflows, deployment reliability, and day-to-day productivity • Collaborate with platform and application engineers to identify friction points and implement incremental improvements • Learn and apply best practices around Infrastructure-as-Code, containerized workloads, and CI/CD pipelines • Use, or are eager to adopt, AI-assisted development tools to improve productivity, and are excited to help explore and integrate LLM-powered solutions that automate internal support and operational workflows • Have opportunities to propose ideas and improvements, with support and mentorship from the team

• Build and operate an AI infrastructure platform: You will design, build, and operate cloud-native infrastructure and platform tooling that accelerates AI development across the company. This includes enabling teams to develop, deploy, and operate AI-powered services safely and efficiently in production environments. • Own the DevOps and infrastructure side of MLOps and Agentic Systems: You will focus on the operational foundations of AI systems, including CI/CD for AI workloads, scalable inference infrastructure, observability, cost management, and reliability. You will establish repeatable patterns and shared services that reduce friction for teams building AI-enabled applications. • Enable AI services, agents, and runtime platforms: You will build and maintain infrastructure to support AI services such as LLM-backed APIs, Model Context Protocol (MCP) servers, and agentic systems used by production applications. You will enable secure tool access, runtime orchestration, and isolation boundaries for AI-driven workloads. • Integrate MLOps capabilities into platform operations: You will apply MLOps concepts to improve platform operations, including using AI-driven approaches for monitoring, alerting, anomaly detection, and incident response across AI and non-AI systems. You will help evolve how the platform observes and operates complex AI-enabled systems at scale. • Establish governance, security, and operational guardrails: You will help define and implement infrastructure-level governance for AI systems, including access controls, deployment policies, auditability, and secure-by-default patterns. You will partner with security and compliance teams to ensure AI infrastructure aligns with organizational risk and regulatory requirements. • Provide technical leadership and enablement: You will act as a technical leader, influencing platform architecture and best practices across teams. You will mentor engineers and work closely with product, data, and application teams to align AI platform capabilities with business goals.

• Ship meaningful improvements to our infrastructure, reliability and developer experience • Take ownership of key platform areas and processes • Partner with product engineers to understand pain points, unblock delivery, and raise standards and improve how we operate

• Develop custom Kubernetes controllers and operators in Go using Kubebuilder. • Design and own CRDs, reconciliation logic, and controller lifecycles. • Implement and evolve GitOps pipelines for platform automation. • Program infrastructure by extending the Kubernetes API rather than relying on standard tooling. • Contribute to architectural design and platform strategy discussions. • Write clean, testable, production-grade Go code with strong testing coverage. • Participate actively in sprint ceremonies, code reviews, and backlog refinement. • Collaborate with cross-functional teams to improve platform maturity, reliability, and developer experience.