AWS DevOps Engineer​/AI Platform Engineer; Hybrid Onsite

AWS Dev Ops 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

Description
- Requirements

Experience:

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

Cloud Expertise:
Deep proficiency in AWS (IAM, Cloud Watch, 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 Lang Chain or Agent Core.

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 Cloud Watch, X-Ray, Bedrock logging, Agent Core 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 Lang Fuse / 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 Cloud Watch (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

Top Skills
- Skill Experience required

Platform Engineering / Dev Ops / SRE8+ years

AWS Cloud Services7+ years

Observability Tools7+ years

AI/LLM Platforms7+ years

Terraform / CI-CD7+ years

Agile Collaboration7+ years