Description

Work with Client team to harden the new AWS AgentCore platform being setup so it can be used by teams to develop and deployAI agents.

Requirements

• Implementation of Agents on Agentcore runtime.
• Implementation of SDLC for Agents (AIDLC) in Agentcore.
• Understanding of Strands or any other Agentic AI framework like Langraph, Langchain or Crew AI.
• Implementation of Bedrock Knowledge Base.
• Implementation of Knowledge Graph & MCP servers.
• Implementation of Agentcore Gateway & Agentcore Identity.
• Implementation of Agentic AI Observability & Agentcore Evaluations.
• Implementation of AWS Bedrock & AWS Bedrock Inference Profile.
• Implementation of AWS Sagemaker.
• AWS Services (Cloud) in General & Terraform

Job responsibilities

1. 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 recommendation

2. 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 rules

3. 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

4. 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.

GlobalLogic estimates the starting pay range for this role to be performed in Reading, PA is $180,000 to $200,000 and reflects base salary only. This pay range is provided as a good faith estimate and the amount offered may be higher or lower. GlobalLogic takes many factors into consideration in making an offer, including candidate qualifications, work experience, operational needs, travel and onsite requirements, internal peer equity, prevailing wage, responsibilities, and other market and business considerations.