Senior AI Platform Engineer

Job Description We are building an AI Engineering function to enable productivity and agentic capabilities across the firm, for end users, developers, and business teams.

As a Senior AI Platform Engineer, you will design and own the shared platform that powers AI systems firm-wide: inference services, agentic platforms, developer tooling, and observability.

This is a financial services environment where data protection, auditability, and regulatory compliance are foundational requirements. You will ensure that AI capabilities are secure by default, auditable end-to-end, and easy for engineering teams to adopt.

You will report to the Head of AI Engineering and partner closely with Security Engineering, AI Integration/Application teams, and core infrastructure groups.

Responsibilities Platform Infrastructure Design, build, and operate the core AI platform, including managed LLM inference services (Amazon Bedrock and related), model access management, versioning, and routing across foundation models

Design and operate shared integration layers, including MCP servers, an MCP registry/gateway, and authorization services that connect AI platforms with core firm systems

Design and operate AI productivity data pipelines and dashboards for usage, cost, and adoption metrics

Design the infrastructure that supports AI-assisted developer tooling (Linux VDI environments), office productivity integrations (M365/Excel), and autonomous agent frameworks

Develop standardized inference and agentic AI platforms that teams can adopt across use cases, including reusable components for RAG, vector databases, and model integration patterns

Security & Guardrails Partner with Security Engineering to embed security controls across the full AI lifecycle

Design, with the AI Security Engineer and infrastructure/platform teams, the controls that prevent destructive agent actions: file system permissions, IAM policies, network allowlists, sandbox configurations, and execution-time policy enforcement

Architect a default-deny posture: agents and tools access only explicitly permitted resources, with no ability to modify or delete production data unless specifically authorized through a human-approval workflow

Implement pre-execution guardrails (hooks, policy engines) that intercept and validate agent actions before they run Ensure AI workloads operate within the corporate network boundary: VPC endpoints, Private Link, no public internet egress for inference traffic

Enablement & Scale Build self-service onboarding so teams can consume AI platform services with appropriate access controls

Design systems that enable cost-effective operation of AI workloads, including quota management and chargeback visibility

Operate firm-wide AI applications and centrally managed AI services

Define reference architectures and patterns that other engineering teams use to build on the platform Qualifications
10+ years as an infrastructure, platform, or systems engineer, with demonstrated experience building and operating shared services consumed by multiple teams, on-premises and on AWSStrong expertise in AWS Bedrock (inference / agent core) and Azure OpenAIStrong expertise in designing and implementing MCP registries, gateways, servers and Authorization flows

Hands-on experience supporting LLM-based workloads in production environments

Experience designing and enforcing AI security controls at the platform layer in a regulated or security-sensitive environment

Track record of building production-quality agentic AI patterns: tool use, function calling, MCP gateway/servers, retrieval-augmented generation, human-in-the-loop workflows

Track record of building production-quality platforms and developer-facing services, with emphasis on usability,…