• Bridge the gap between AI capabilities and patient needs • Take a hands-on leadership role in integrating AI features • Collaborate with Data and Product teams on use cases and build robust RAG pipelines • Partner with DevOps to leverage cloud infrastructure for deployment • Implement monitoring and observability practices for AI services • Own the AI engineering strategy and mentor the engineering team on AI practices

• Design, build, and evolve internal platform services by understanding how Chainguard engineers use shared infrastructure, ensuring our 'Factory' capabilities work seamlessly across the organization. • Implement agent observability by capturing and analyzing structured traces, making AI-driven workflows (like automated CVE remediation) debuggable and reliable in production. • Optimize our monorepo CI/CD pipeline to maintain high-performance DORA metrics, focusing on improving build stability and reducing flake rates. • Master context engineering by validating patterns for agent skills and memory, enabling agents to make consistent decisions across complex engineering workflows. • Contribute to a productivity-driven roadmap with clear outcomes, helping the team prioritize tradeoffs to remove 'shadow production' risks and deliver standardized 'paved road' blueprints.

• Develop and maintain modular components and subsystems. • Contribute to the design, creation, and measurement of products that directly impact the lives of thousands of people every day. • Participate in definitions together with principals and other teams. • Identify and explore opportunities for improvement to ensure a high-quality product and an excellent user experience. • Optimize application performance to ensure fast loading times and a smooth user experience.

• Serve as the technical owner for major systems or platforms within an established strategic direction • Define and evolve architectural patterns, design standards, and reference implementations • Lead design reviews and guide teams toward sound technical decisions • Identify and resolve complex technical risks spanning multiple components or teams • Design and implement critical or high-risk components • Review and elevate code quality across teams through deep technical feedback • Prototype and validate new approaches before broader adoption • Ensure systems meet requirements for scalability, reliability, security, and maintainability • Lead the technical design of AI-enabled capabilities using approved models, frameworks, and platforms • Establish patterns for AI integration and orchestration • Evaluation, testing, and validation • Safety, governance, and human-in-the-loop controls • Guide teams in applying AI responsibly within production systems • Act as a technical authority and escalation point across multiple teams • Mentor Senior and mid-level engineers by setting a high technical bar • Influence engineering practices, tooling, and standards through example and guidance • Collaborate with product, security, and platform leaders to align technical solutions with mission needs.