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    Home/Jobs/GenAI / Agentic AI Engineer

    GenAI / Agentic AI Engineer

    Infosys

    Bengaluru
    6-12 years
    Today
    ₹23–44 LPA
    Full-time
    Onsite

    Skills Required

    LangChain
    LLM
    RAG
    Gen AI
    Python
    AWS Bedrock
    LangGraph
    Git
    Docker
    CI/CD pipelines

    Description

    Infosys Limited is seeking a GenAI / Agentic AI Engineer with 6-12 years of experience.

    Company: Infosys Limited

    Role: GenAI / Agentic AI Engineer

    Location: Bangalore

    Experience:

    • 6 - 12 Years

    Key Skills:

    • Python
    • AWS Bedrock
    • LangChain
    • LangGraph
    • LLMs
    • Python - Core concepts: OOP, typing, modules, pytest/unittest, fixtures, basic async
    • AWS Bedrock - Model invocation (Claude/Titan, etc.), Knowledge bases or retrieval integration, basic understanding of guardrails & access controls
    • Generative AI - Prompting (system, few-shot), RAG types and approaches, Basic evaluation awareness
    • DevOps Basics - Git, PRs, CI pipelines, Docker basics, artifacts

    Qualification:

    • Bachelor of Engineering

    Role Focus:

    • Develop GenAI features and workflows using Python and AWS Bedrock
    • Implement RAG pipelines (chunking, embeddings, retrieval, grounding)
    • Build single/multi-step agent workflows using LangChain / LangGraph
    • Integrate LLMs with APIs, tools, and enterprise applications
    • Write tested, maintainable Python code with CI/CD pipelines
    • Monitor basic latency, token usage, and cost metrics
    • Collaborate with architects, seniors, and QA teams

    Other:

    • Service Line: Topaz COE unit
    • Job ID/Reference Code: INFSYS-EXTERNAL-246701

    Prepare for this role

    Recommended resources to build the skills for this position. Sponsored.

    Python for Everybody Specialization

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    Coursera

    Intermediate Python covering classes, inheritance, APIs, and data processing.

    Generative AI with Large Language Models

    Coursera

    Comprehensive LLM course covering transformer architecture, fine-tuning, RLHF, and deployment.

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