Senior Technical Architect Technology Architect 3405

Job description

Overall Objectives of Job: We are seeking a senior AI Platform Engineer to build and operate the AI platform layer – the runtime and governance environments that all teams plug into. The role builds and runs model routing and caching, shared RAG, MCP interfaces, Spec Kits, AI frameworks, Skills, and prompt management, ensuring AI agents run in a financially responsible and technically sound environment. As an opinionated curator of the AI tool and framework landscape, the AI Platform Engineer decides what becomes AI Factory standard, what stays sandboxed, and what gets retired, providing a plug-and-play surface with AI FinOps and safety guardrails baked in, not bolted on. The platform forms the runtime and governance backbone that service teams and AI Use Case Delivery build on top of. Qualifications & Experience Bachelor’s degree in Computer Science, Engineering, or a related field. 8–12 years of overall engineering experience, with strong hands-on platform, infrastructure, or developer-platform building. Proven experience building and operating shared runtime, platform, or governance environments that multiple teams depend on. Hands-on experience with GenAI platform components – model routing and caching, shared RAG, MCP interfaces, agent / AI frameworks, prompt management, and Skills / Spec Kits. Strong cloud-native engineering skills on at least one major platform (Azure, AWS, or GCP), including cost and performance optimization ( Experience embedding safety, security, and responsible-AI guardrails into a platform by design – baked in, not bolted on. A hands-on platform builder with an enablement mindset, comfortable making build-vs-buy and vendor decisions under uncertainty. Role & Responsibilities Build and operate the AI platform layer – the runtime and governance environments all teams plug into. Build and run model routing and caching, shared RAG, and MCP interfaces. Provide and maintain Spec Kits, AI frameworks, Skills, and prompt management as shared capabilities. Ensure AI agents run in a financially responsible and technically sound environment. Act as the opinionated curator of the AI tool and framework landscape – set the AI Factory standard, sandbox the rest, and retire what no longer fits. Provide a plug-and-play platform surface with AI FinOps and safety guardrails baked in, not bolted on. Form the runtime and governance backbone that service teams and AI Use Case Delivery build on top of. Make build-vs-buy and vendor decisions under uncertainty, balancing enablement, cost, and risk. Partner with Forward Deployed Engineers, AI Engagement Managers, and service teams so the platform meets real delivery needs. Embed security, responsible-AI, and governance standards into the platform by design. Key Result Areas Reliability, performance, and adoption of the shared AI platform across teams. Financial efficiency of AI workloads and effectiveness of safety guardrails. Clarity and adoption of the curated AI tool / framework standard (standard vs sandboxed vs retired). Speed and ease with which service teams and Use Case Delivery build on the platform. Security, responsible-AI, and governance posture of the platform – baked in, not bolted on.