Senior AWS Agentcore Platform Engineer

This role owns the observability, cost governance, and security foundation for the company's enterprise AI agent ecosystem running on AWS Bedrock and AgentCore. You will design and implement distributed tracing for agentic/LLM workloads, build per-team cost visibility dashboards and automated reporting, define a full alerting framework (P1–P4) with linked runbooks, and architect a scalable, ABAC-based identity and access model using Terraform. You'll also evaluate and recommend the target-state monitoring stack (AWS-native vs. third-party) and deliver a post-deployment validation pipeline for agents and MCP servers. Responsibilities Assess CloudWatch, X-Ray, Bedrock logging, AgentCore traces vs. agentic workflow requirements; produce gap analysis, Setup observability in Dynatrace Design post-deployment validation pipeline for agents & MCP servers (deployment health + tool registration checks) Implement distributed tracing & structured logging: LLM decisions, tool selections, sub-agent calls, MCP interactions Evaluate LangFuse / LiteLLM proxy vs. AWS-native; deliver target-state observability architecture recommendation Cost Tracking & TCO Extend tagging taxonomy to cover agent runtimes, MCP servers, vector DBs, Bedrock token consumption per namespace Design cost visibility model: aggregate agent, MCP, vector DB, and Bedrock token costs per team/department Build CloudWatch (or equivalent) dashboards for per-team spend; configure AWS Budgets with alerting thresholds Monitoring & Alerting Define P1–P4 alerting rules: deployment failures, runtime errors, tool invocation failures, MCP connectivity issues Integrate alert notifications to Microsoft Teams channels and email; route by resource ownership tags Author runbooks linked to every alert; publish in Confluence for developer self-service resolution Evaluate AWS-native vs. third-party monitoring stack; deliver recommendation aligned to observability architecture Security & Access Control Assess current IAM + tagging approach for multi-team isolation; identify scalability gaps and risks Evaluate Cedar policy engine (AgentCore) for fine-grained tool access control; document enterprise-scale gaps Design scalable ABAC-based identity model for multi-team isolation without IAM policy sprawl; deliver Terraform modules Must Have 8+ years in Platform Engineering, DevOps, or SRE Deep AWS proficiency: IAM, CloudWatch, X-Ray, Lambda, Bedrock Hands-on experience with distributed tracing and structured logging (Dynatrace, Jaeger, or Honeycomb) Advanced Terraform skills and CI/CD pipeline design Experience designing ABAC-based IAM models and tagging taxonomies for multi-team environments Familiarity with Agile workflows and tools like Confluence and Microsoft Teams Exposure to LLM lifecycle concepts: prompt execution, token usage, tool selection, sub-agent orchestration Experience with LangChain, AgentCore, LangFuse, or LiteLLM Familiarity with the Cedar policy engine for fine-grained access control Experience with LLMOps or AI/agent-specific observability patterns Background evaluating AWS-native vs. third-party monitoring stacks #J-18808-Ljbffr