Agentic AI Developer (AWS)

Cognizant; Chennai District; Hybrid; INR 1,500,000 - 2,800,000; Full time

Job summary

Cognizant in Chennai is seeking a Senior Developer to build Agentic AI and GenAI applications on AWS. You will design and implement RAG pipelines, AI agents, and multi-step workflows that connect with enterprise systems and APIs.

The role emphasizes strong Python and AWS hands-on experience, familiarity with LangGraph and AutoGen, and a solid grasp of embeddings and vector search to power scalable AI solutions.

Qualifications

• 5–8 years of software development experience, with 1–3 years in GenAI/LLM/RAG
• Proficient Python and hands-on AWS experience (Bedrock, Lambda, SageMaker, OpenSearch)
• Experience with LangGraph or AutoGen frameworks
• Understanding of RAG, embeddings and vector search

Responsibilities

• Build agentic applications using Amazon Bedrock and foundation models
• Develop AI agents with tool use, memory, and reasoning capabilities
• Create RAG pipelines and vector search integrations
• Test, troubleshoot, and support production AI deployments

Skills

Python

AWS Bedrock

RAG

Vector search

Embeddings

API integration

Testing

Tools

LangGraph

AutoGen

OpenSearch

Lambda

SageMaker

Bedrock

Job description

Senior Developer Agentic AI (AWS)

Experience: 5 to 8 years (1 to 3 years in GenAI/LLM/RAG)

Location: Chennai

We're looking for a Senior Developer to build Agentic AI and GenAI applications on AWS. You'll develop RAG pipelines, AI agents, and multi-step workflows using frameworks like LangGraph and AutoGen, integrating them with enterprise systems and APIs.

You'll do:

• Build agentic applications using Amazon Bedrock and foundation models
• Develop AI agents with tool use, memory, and reasoning capabilities
• Create RAG pipelines and vector search integrations
• Test, troubleshoot, and support production AI deployments

You should have:

• Strong Python skills and hands-on AWS experience (Bedrock, Lambda, SageMaker, OpenSearch)
• Familiarity with LangGraph, AutoGen, or similar frameworks
• Understanding of RAG, embeddings, and vector search