SME - AWS IAC, Terraform,Python

Job Summary

We are seeking a skilled AWS Bedrock Engineer to design, develop, deploy, and support Generative AI and Agentic AI solutions on AWS. The candidate will leverage Amazon Bedrock, Foundation Models (Claude, Amazon Nova, Mistral, etc.), RAG architectures, Knowledge Bases, AI Agents, and AWS serverless services to build enterprise-grade GenAI applications. Internal learning resources emphasize Bedrock-based RAG, Knowledge Bases, AI Agents, LangChain, and enterprise AI implementations.

Key Responsibilities

Key Responsibilities Generative AI Solution Development Design and develop enterprise GenAI applications using Amazon Bedrock. Build conversational AI assistants, document intelligence, summarization, and knowledge search solutions. Develop AI-powered workflows and Agentic AI use cases. Integrate foundation models through Bedrock APIs. RAG & Knowledge Management Design and implement Retrieval Augmented Generation (RAG) architectures. Configure and manage Knowledge Bases for Amazon Bedrock. Integrate enterprise documents, SharePoint repositories, and business knowledge sources. Optimize vector search and grounding mechanisms for improved accuracy. Bedrock learning paths specifically reference RAG implementations and Knowledge Bases. AI Agent Development Build and maintain AI Agents using Bedrock Agent capabilities. Implement multi-agent orchestration and workflow automation. Integrate AI Agents with ITSM tools, automation platforms, and business applications. Support Agentic AI implementations similar to enterprise use cases using Bedrock and orchestration frameworks. AWS Cloud Integration Integrate Bedrock solutions with AWS Lambda, API Gateway, S3, DynamoDB, EventBridge, and Step Functions. Develop scalable and serverless GenAI architectures. Implement secure API integrations and cloud-native deployments. AWS enablement content references these AWS services alongside Bedrock implementations. MLOps & Governance Support model lifecycle management and AI governance. Monitor AI application performance, latency, and cost. Implement prompt engineering standards and guardrails. Ensure compliance with enterprise security and responsible AI practices.

Skill Requirements

Required Technical Skills AWS Services Amazon Bedrock AWS Lambda API Gateway S3 DynamoDB IAM CloudWatch Step Functions AI & GenAI Foundation Models (FM) Prompt Engineering RAG Architecture AI Agents Knowledge Bases LLM Evaluation AI Governance Development Python REST APIs JSON Git CI/CD Frameworks LangChain LangGraph (Preferred) Streamlit FastAPI Bedrock learning content within the organization highlights Python, LangChain, Streamlit, RAG, AI Agents, Knowledge Bases, and application development.

Other Requirements

Preferred Skills AWS SageMaker Amazon Q Vector Databases OpenSearch MLOps Kubernetes Docker Agentic AI Frameworks Enterprise Automation Platforms ServiceNow Integration Enterprise AI training references Amazon Q, SageMaker, Bedrock Knowledge Bases, enterprise AI workflows, and MLOps capabilities.