Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Strong experience with GCP IAM and identity (role/permission modeling, service accounts, federation patterns, access governance).
Hands-on expertise with key GCP services such as Cloud Run/Cloud Functions, Pub/Sub, and GKE.
Proficiency in Terraform and programming/scripting in Python or Go (automation, tooling, CI/CD integration).
Experience implementing guardrails at scale (IAM Conditions/deny policies, policy-as-code, centralized logging/audit, key management patterns).
Demonstrated ability to deliver secure, compliant solutions and influence technical decisions across cross-functional teams.
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Preferred qualifications, capabilities, and skills
Experience with AWS and/or Azure (multi-cloud operating models and patterns).
Strong containerization/orchestration experience (Docker, Kubernetes), including production-grade GKE operations.
Solid understanding of OAuth2, OIDC, and SAML; secure service-to-service identity patterns.
Good understanding of LLM and other AI/ML frameworks which can be used in AIOPS
Relevant certifications (e.g., Google Professional Cloud Architect and/or Professional Cloud Security Engineer).
