AWS Architect (Client: Penske)

Highbrow LLC; Reading (Berks County); On-site; USD 120,000 - 150,000; Full time

Job summary

Highbrow LLC is seeking an AWS Architect to work onsite in Reading, PA. The role involves implementing various AWS services and integrating advanced Agentic AI frameworks.

Responsibilities include establishing cloud observability, cost tracking, and designing monitoring & alerting systems across deployment health checks. The ideal candidate should be well-versed in Terraform and have a strong understanding of AWS services.

Qualifications

• Experience with AWS services and Agentic AI frameworks.
• Strong knowledge in Terraform and cloud observability.
• Proven ability in cost tracking and monitoring.

Responsibilities

• Implement AWS Services for agent workflows and observability.
• Design and automate cost visibility models.
• Define alerting rules and author runbooks for incidents.

Skills

Implementation of AWS Services

Terraform

Agentic AI frameworks

CloudWatch

Cost Tracking & TCO knowledge

Tools

AWS Sagemaker

Dynatrace

Confluence

Job description

AWS Architect (Client: Penske)

Location: Reading PA (4-5 days onsite)

Job Description

1. Implementation of Agents on Agentcore runtime
2. Implementation of SDLC for Agents (AIDLC) in Agentcore
3. Understanding of Strands or any other Agentic AI framework like Langraph, Langchain or Crew AI
4. Implementation of Bedrock Knowledge Base
5. Implementation of Knowledge Graph
6. Implementation of MCP servers
7. Implementation of Agentcore Gateway
8. Implementation of Agentcore Identity
9. Implementation of Agentic AI Observability
10. Implementation of Agentcore Evaluations
11. Implementation of AWS Bedrock
12. Implementation of AWS Bedrock Inference Profile
13. Implementation of AWS Sagemaker
14. AWS Services (Cloud) in General
15. 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