AI Engineer / Cloud Engineer

AI Engineer / Cloud Engineer Core Stack: AWS Bedrock AgentCore • Azure AI Foundry • MCP Governance Function: Foundations / Infrastructure & Operations Level: Senior / Staff Type: Full-time Location: Remote / Hybrid Reports to: Director, AI Architecture About the Role The Foundations team serves as the enterprise AI governance control plane for Infrastructure & Operations (I&O), responsible for the infrastructure, observability, security, and policy layer across a multi-cloud AI agent ecosystem. We are seeking a senior AI/Cloud Engineer to design, build, and operate production-grade AI agent infrastructure across Amazon Web Services Bedrock AgentCore and Microsoft AI Foundry, with deep integration across MCP (Model Context Protocol) connectors, LLM gateways, and enterprise data systems. This role sits at the intersection of AI platform engineering, cloud infrastructure, governance, and enterprise security. You will partner closely with the I&O Architecture Engineering team to ensure AI agents are observable, measurable, secure, and fully governed across the enterprise estate. Key Responsibilities 1. Agent Infrastructure & Platform Engineering Design and deploy production AI agent workloads on AWS Bedrock AgentCore, including runtime configuration, memory stores, and DataDog observability instrumentation. Build and maintain Azure AI Foundry agent pipelines with Application Insights telemetry, Azure APIM-based token attribution, and Azure Monitor integration for safety and red-teaming signals. Architect MCP connector infrastructure, including tool-call routing, RBAC enforcement, OAuth2 / Entra ID scoping, and end-to-end audit logging. Maintain and evolve the enterprise LLM gateway as the centralized routing, policy enforcement, and instrumentation layer across Bedrock, Azure OpenAI, and Claude-based endpoints. 2. Governance, Security & Observability Instrument agent systems to capture Tier 1 audit KPIs such as tool-call completeness, policy violations, RBAC coverage, and authentication failure rates aligned with compliance requirements. Define per-connector security policies ensuring Finance, HR, Legal, and Client data systems remain fully governed and least-privileged. Build unified observability dashboards across CloudWatch, Azure Monitor, and DataDog for AI system health and executive reporting. Participate in CDR (Critical Design Review) processes for all AI agent deployments, ensuring adherence to reliability, security, and observability standards. Design SOC 2-aligned audit log pipelines for all agent tool-calls to support compliance and forensic traceability. 3. Integration & Platform Interoperability Integrate AI agent systems with enterprise platforms such as ServiceNow, DataDog, Apptio/Cloudability, and internal data platforms via MCP connectors and REST APIs. Support CI/CD automation for AI agents using GitHub Actions, including environment promotion, rollback strategies, and pipeline replay mechanisms. 4. AI Quality & Evaluation Define and instrument agent KPIs including task completion rate, hallucinated tool-call detection, escalation rate, and context efficiency metrics. Leverage AWS AgentCore evaluation frameworks and Azure AI Foundry evaluation tooling to assess groundedness, safety, tool accuracy, and reliability. Build golden dataset regression suites to detect performance degradation across model updates, prompt changes, and connector modifications. Required Qualifications Platform Experience (Must Have) AWS Bedrock AgentCore (production workloads) Azure AI Foundry (agent pipelines & evaluation systems) AWS Lambda / EKS Azure API Management (APIM) CloudWatch (metrics, logs, traces) Azure Monitor + Application Insights LLM Gateway architecture experience MCP (Model Context Protocol) servers OAuth2 / Entra ID / IAM-based credential scoping Engineering Skills 5+ years software/platform engineering experience; 2+ years in AI/ML infrastructure or LLM-based systems Strong proficiency in Python; TypeScript/Node.js preferred for orchestration layers Experience with REST APIs, async processing, and event-driven architectures Hands-on CI/CD experience (GitHub Actions preferred) Infrastructure-as-Code and container-based deployment experience Strong observability engineering experience (metrics, logs, tracing in