AWS DevOps Engineer / AI Platform Engineer (Hybrid Onsite)

Job Title

AWS DevOps Engineer / AI Platform Engineer (Hybrid Onsite)

Reading, PA

6 Months

Virtual / In-Person

• Hybrid 2-3 days/wk at client office

• Is your candidate willing to relocate for position

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

Skill

Experience required

8+ years

7+ years

7+ years

7+ years

7+ years

7+ years

Contact: Sameer - View email address on us.fitly.work - Seven one nine - two three nine - Five five five five