Senior AI Platform Engineer

As a Senior AI Platform Engineer, you will take ownership of the core platform underpinning enterprise AI systems. This includes building and maintaining shared services such as model inference, agent frameworks, developer tooling, and monitoring capabilities. Operating within a highly regulated environment, you will ensure that all AI solutions meet strict standards for data privacy, security, and auditability. The platform you build will be designed to be secure by default, transparent in operation, and straightforward for teams to adopt. You will work closely with the Head of AI Engineering and collaborate with teams across security, infrastructure, and application development. Key Responsibilities Design, implement, and manage the organization’s AI platform, including managed inference services (e.g. Amazon Bedrock), model routing, version control, and access management Build and maintain shared integration layers that connect AI systems with internal platforms, including MCP-based services, registries, gateways, and authentication/authorization mechanisms Develop analytics pipelines and reporting for tracking AI usage, cost efficiency, and adoption across teams Create infrastructure to support AI-enabled development environments, productivity tool integrations (e.g. Microsoft 365), and autonomous agent systems Deliver reusable frameworks and standardized components for use cases such as RAG, vector search, and model orchestration Define scalable architecture patterns to enable widespread adoption across engineering teams Security & Governance Collaborate with security specialists to embed controls throughout the AI lifecycle Help design and enforce safeguards that prevent unintended or harmful agent behavior, including permissions, identity controls, sandboxing, and runtime policy enforcement Establish a "least privilege" model where systems and agents only access explicitly approved resources Introduce pre-execution validation mechanisms (e.g. policy engines or hooks) to govern agent actions Ensure AI systems operate fully within secure network boundaries, avoiding public exposure of inference workloads Enablement & Adoption Develop self‑service capabilities that allow teams to onboard onto the AI platform with appropriate governance controls Design and implement cost management strategies, including usage quotas and transparent chargeback models Support and operate centrally managed AI services and enterprise‑wide applications Produce reference architectures, best practices, and guidance to support consistent platform usage Required Experience Extensive experience in platform, infrastructure, or systems engineering roles, particularly delivering shared services at scale across cloud and on‑prem environments Strong hands‑on knowledge of AWS (including Bedrock) and Azure OpenAI services Experience designing and implementing MCP‑based architectures, registries, gateways, and secure access flows Proven track record supporting LLM‑powered applications in production environments Demonstrated experience implementing security and governance controls for AI systems, ideally in regulated settings Experience building and deploying agent‑based AI solutions, including tool integration, function calling, RAG pipelines, and human‑in‑the‑loop processes Strong background in building developer‑facing platforms with a focus on usability, reliability, and standardization Excellent communication skills with the ability to collaborate across technical and non‑technical teams #J-18808-Ljbffr