Agentic AI Engineer with AWS

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.

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

Amazon Bedrock & AgentCore

Strands and/or LangGraph

MCP Integrations

AgentOps & AI Observability

DeepEval, RAGAS, Bedrock Evaluation

RAG and Knowledge Bases

REST APIs & Enterprise Integrations

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