Quick Overview
Seniority
Mid Senior
Work mode
Remote
Location
United States
Posted
Yesterday
DockerDynamoDBMicroservicesAPI GatewayAWSOAuthCDKKubernetesPythonRESTTerraform
Job Description
Job Title: Agentic AI Engineer with AWS
Location: Remote
Duration: Long Term Contract
JD:-
Main focus:- Agentic AI + LangGraph/LangChain + MCP (Model Context Protocol) + RAG + AgentOps + Python and Bedrock/AgentCore + AWS
Responsibilities:-
- Design and implement AI agents and multi-agent workflows using Strands, LangGraph, and Amazon Bedrock AgentCore.
- Build MCP integrations with enterprise systems and APIs.
- Implement evaluation, monitoring, tracing, and operational controls using OpenTelemetry, ADOT, CloudWatch, DeepEval, RAGAS, and Bedrock Evaluation.
- Optimize latency, reliability, scalability, and cost of production AI solutions.
- Define release, governance, and operational processes for agent lifecycle management.
- AgentCore-based orchestration, guardrails, evaluation, observability, and governance are foundational platform capabilities.
- Working hands on understanding of Evaluation framework like DeepEvals, Ragas, and Bedrock evals
- Develop and manage MCP (Model Context Protocol) integrations with enterprise systems and tools.
Required Technical Skills
- Cloud skills
- AWS Architecture & Multi-Account Strategy
- AWS Organizations, Control Tower & SCPs
- AWS CDK and/or Terraform
- GitLab/GitHub CI/CD
- Docker & Amazon ECR
- IAM Identity Center, OAuth/OBO
- DynamoDB, S3
- CloudWatch, CloudTrail, OpenTelemetry, ADOT
- VPC Endpoints / PrivateLink
- Cloud Security & Production Operations
Agentic AI
- Amazon Bedrock & AgentCore
- Strands and/or LangGraph
- MCP Integrations
- AgentOps & AI Observability
- DeepEval, RAGAS, Bedrock Evaluation
- RAG and Knowledge Bases
- REST APIs & Enterprise Integrations
Preferred Qualifications
- AWS Certified Solutions Architect / AWS DevOps Engineer Certification.
- Experience building enterprise-scale Agentic AI platforms with strong deployment experience
- Strong expertise in Python and cloud-native application development.
- Experience with event-driven architectures and microservices.
- Exposure to AI governance, security, and responsible AI practices.
- LangGraph / LangChain expertise
- AWS Bedrock Knowledge Bases
- Amazon API Gateway
- AWS Lambda, Step Functions, API gateway
- Vector databases and RAG architectures
- Kubernetes / EKS
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