Job Title AWS DevOps Engineer / AI Platform Engineer (Hybrid Onsite) Location Reading, PA Duration 6 Months Interview Type Virtual / In-Person Note Hybrid 2-3 days/wk at client office Is your candidate willing to relocate for position

Description Requirements Experience:

8+ years in Platform Engineering, DevOps, or Site Reliability Engineering (SRE).

Cloud Expertise:

Deep proficiency in AWS (IAM, CloudWatch, Bedrock, Lambda).

Observability Tools:

Proven experience with Dynatrace, Jaeger, or Honeycomb, and distributed tracing standards.

AI/LLM Interest:

Familiarity with the LLM lifecycle, including prompt execution, token usage, and frameworks like LangChain or AgentCore.

Automation:

Advanced experience with Terraform and CI/CD pipeline design.

Collaboration:

Experience working in an Agile environment with integrated tools like Microsoft Teams and Confluence.

User this when submitting candidates:

Also please check if the next candidate has some experience with at least 50% of below items: Implementation of Agents on Agentcore runtime Implementation of Agentic SDLC 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 Implementation of MCP servers in Agentcore Implementation of Agentcore Gateway Implementation of Agentcore Identity Implementation of Agentic AI Observability Implementation of Agentcore Evaluations Implementation of AWS Bedrock Implementation of AWS Bedrock Inference Profile Implementation of AWS Sagemaker AWS Services (Cloud) in General Terraform Deliverables 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 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 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 Top Skills Skill Experience required Platform Engineering / DevOps / SRE 8+ years AWS Cloud Services 7+ years Observability Tools 7+ years AI/LLM Platforms 7+ years Terraform / CI-CD 7+ years Agile Collaboration 7+ years

Recruiter Contact:

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