Senior AI Enablement Engineer

AI Engineer What You'd Be Doing

• 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.

What We Need to See

• 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.

Ways to Stand Out from the Crowd

• Experience building or contributing to an internal AI enablement function, center of excellence, or developer experience program.
• Hands-on experience with LLM Agents, RAG pipelines, vector databases (pgvector, OpenSearch, Pinecone, or similar), and prompt orchestration frameworks such as LangChain or LlamaIndex.
• Familiarity with AI FinOps tooling, cost attribution models, and LLM API usage reporting at an organizational scale.
• Experience with AI governance frameworks including acceptable use policies, audit logging, PII redaction pipelines, and responsible AI practices in regulated enterprise environments.
• Background in financial services or insurance with an understanding of compliance constraints on AI tool usage an