Cloud Architect

Title: AWS ArchitectLocation: Reading PA (4-5 days onsite)Job Description1. Implementation of Agents on Agentcore runtime2. Implementation of SDLC for Agents (AIDLC) in Agentcore3. Understanding of Strands or any other Agentic AI framework like Langraph, Langchain or Crew AI4. Implementation of Bedrock Knowledge Base5. Implementation of Knowledge Graph6. Implementation of MCP servers7. Implementation of Agentcore Gateway8. Implementation of Agentcore Identity9. Implementation of Agentic AI Observability10. Implementation of Agentcore Evaluations11. Implementation of AWS Bedrock12. Implementation of AWS Bedrock Inference Profile13. Implementation of AWS Sagemaker14. AWS Services (Cloud) in General15. Terraform Job Responsibilities1. Observability• 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 recommendation2. 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• Automate cost reports delivered via email / Microsoft Teams; implement anomaly detection rules3. 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 architecture4. 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