Senior AI Platform Engineer

Responsibilities

• Design 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
• 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
• Partner with Security Engineering to embed security controls across the full AI lifecycle
• Implement pre-execution guardrails (hooks, policy engines) that intercept and validate agent actions before they run
• Enable 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 AWS
• Strong expertise in AWS Bedrock (inference / agent core) and Azure OpenAI
• Strong 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, standardization, and reliability
• Strong written and verbal communication skills, with the ability to work effectively across security, application, and infrastructure teams

Preferred Qualifications

• Experience in financial services, healthcare, or another heavily regulated industry
• Experience with Microsoft M365 Copilot / Copilot Agents
• Experience building observability pipelines (Splunk, ELK, Datadog, or Grafana)
• Familiarity with containerized and Kubernetes-based environments
• Experience with model fine-tuning workflows and ML lifecycle tooling
• Familiarity with DLP tooling and data classification frameworks

Pay

The base salary range for this position is $200,000 - $325,000 per year.