Artificial Intelligence Engineer - This role sits at the intersection of Developer Experience, Platform Engineering, DevOps, and Applied AI, focused on integrating AI-assisted tooling, LLM workflows, and automation into the software development lifecycle. The ideal candidate will bring hands-on experience with LLM APIs, AI developer tooling, Python automation, cloud infrastructure, and developer productivity metrics, while helping shape internal AI governance, adoption frameworks, and scalable engineering golden paths. Key ResponsibilitiesChampion an AI-first engineering culture through reusable playbooks, golden-path templates, and best practices that improve developer productivity. Define and measure impact using DORA and SPACE metrics, driving improvements across engineering workflows. Build reusable patterns for:AI-assisted code generationPull request automationTest generationDebugging workflowsDeveloper self-service enablementEvaluate, prototype, and lead POCs for AI tools, providing recommendations based on ROI, usability, technical merit, and stakeholder feedback. Manage AI usage governance, including:Token budgeting and cost controlsUsage monitoring and reportingPII redaction guardrailsAudit logging and responsible AI practicesDevelop lightweight AWS-based infrastructure supporting LLM pipelines, APIs, and AI integrations. Integrate AI tooling into GitLab CI/CD, GitOps workflows, and developer platforms. Instrument AI workflows with observability metrics and usage signals to support adoption and optimization. Partner with engineering leadership and cross-functional stakeholders to drive AI enablement initiatives. Required Qualifications8 years of experience in Platform Engineering, DevOps, Developer Experience, or related engineering roles. Strong hands-on experience with LLM APIs and AI-assisted developer tooling in production or enterprise settings. Strong Python development skills for automation and AI workflow integrations. Experience evaluating, procuring, or governing AI/SaaS tools, including vendor assessments, licensing, and cost management. Practical daily usage of tools such as GitHub Copilot, Cursor, Claude Code, ChatGPT, or similar. Experience designing scalable developer workflows, internal platforms, or self-service engineering capabilities. Strong AWS experience, ideally including:BedrockAPI GatewayManaged AI/Cloud ServicesExperience with Terraform, Helm, GitOps, and CI/CD pipelines (GitLab preferred). Strong observability mindset with experience defining meaningful metrics and instrumentation. Excellent communication and stakeholder management skills. Preferred / Nice to HaveExperience contributing to an AI Enablement function, Developer Experience program, or Center of Excellence. Hands-on experience with:LLM AgentsRAG pipelinesVector databases (Pinecone, pgvector, OpenSearch, etc. )LangChain / LlamaIndexExposure to AI FinOps, usage reporting, and cost attribution models. Knowledge of AI governance and security, including:OWASP Top 10 for LLMsPrompt injection risksModel supply chain securityResponsible AI controlsFinancial services or insurance domain experience is a plus.