Drive an AI-first culture through internal playbooks and "golden-path" templates while measuring impact via DORA and SPACE metrics.
Manage AI costs through token budgeting and usage tracking alongside guardrails like PII redaction and audit logging.
Build and document reusable patterns for code generation, PRs, testing, and debugging to optimize the end-to-end developer lifecycle.
Conduct POCs and provide recommendations for AI tools based on ROI, technical merit, and stakeholder feedback.
Manage lightweight AWS infrastructure including API Gateways and LLM pipelines while integrating tools with CI/CD and GitLab.

8+ years in platform engineering, DevOps, developer experience, or a closely related technical discipline.
Demonstrated hands-on experience with LLM APIs and AI developer tooling in production or organizational contexts
Experience evaluating, procuring, or governing AI/SaaS tools at an organizational level, including vendor assessment, license management, and cost governance.
Strong Python skills for automation, tooling, and lightweight AI workflow and integration development.
Practical, daily use of AI-assisted development tools (GitHub Copilot, Cursor, Claude Code, ChatGPT, or similar) in your own engineering workflows.
Experience designing developer workflows, internal platforms, or engineering self-service capabilities with a focus on adoption and usability.
Solid AWS experience with familiarity with Bedrock, API Gateway, or equivalent managed AI and cloud services.
Strong observability mindset with the ability to instrument AI tooling and workflows with meaningful metrics and usage signals.
Infrastructure-as-code familiarity (Terraform, Helm) and experience working within GitOps and CI/CD environments, with GitLab CI preferred.
Excellent communication and stakeholder management skills, with the ability to translate technical findings into clear recommendations for engineering leadership and business audiences.

Senior AI Enablement Engineer

Key skills