AI / LLM Engineer

Infosys; Pune District; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Infosys in Pune is seeking an experienced AI/ML engineer to design and implement LLM-powered workflows for summarization, narrative generation, and extraction across enterprise use cases.

You will build retrieval-augmented generation pipelines, integrate with cloud AI services, containerize components, and establish governance, logging, and guardrails while collaborating with backend and DevOps teams.

Qualifications

• Strong hands-on Python programming experience and practical exposure to LLM-based application development.
• Experience with RAG, vector databases, embeddings, prompt engineering, evaluation frameworks and AI service integration.
• Experience with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI or equivalent tools.
• Working knowledge of REST APIs, microservices, SQL, structured data concepts, Git workflows, testing and software engineering practices.
• Understanding of document extraction, semantic search, NLP, retrieval quality, hallucination risk, prompt safety and AI evaluation methods.
• Ability to build production-oriented AI components rather than isolated proof-of-concept demos.

Responsibilities

• Design and implement LLM-powered workflows for summarization, narrative generation, classification, extraction, contextual reasoning, explanation and reviewer-assist use cases.
• Build retrieval-augmented generation pipelines including document ingestion, chunking, embedding generation, metadata tagging, vector indexing, retrieval tuning and grounded response generation.
• Develop reusable prompt templates, prompt versions, context builders, response schemas, evaluation routines and AI orchestration services.
• Integrate with enterprise AI services such as Azure OpenAI, Azure AI Foundry, OpenAI APIs, Google Gemini, Anthropic, Hugging Face or equivalent approved platforms.
• Implement AI run logging, prompt/model metadata capture, evidence citations, output traceability, reviewer feedback capture and human-in-the-loop controls.
• Build AI evaluation routines for answer quality, retrieval quality, hallucination checks, regression testing, consistency and groundedness.
• Collaborate with backend and DevOps teams to containerize AI services, deploy them securely, monitor usage, track costs and troubleshoot production issues.
• Support responsible AI practices such as prompt injection checks, data leakage prevention, policy-based guardrails and AI output validation.

Skills

Python

LLM Development

RAG

Vector DB

Prompt Engineering

AI Integration

LangChain

REST APIs

Microservices

NLP

Education

Bachelor of Engineering

Master of Engineering

Tools

LangChain

LlamaIndex

Semantic Kernel

AutoGen

CrewAI

Job description

Educational Requirements

• Bachelor of Engineering,Master Of Engineering

Service Line

• Global Delivery

Responsibilities

• Design and implement LLM-powered workflows for summarization, narrative generation, classification, extraction, contextual reasoning, explanation and reviewer-assist use cases.
• Build retrieval-augmented generation pipelines including document ingestion, chunking, embedding generation, metadata tagging, vector indexing, retrieval tuning and grounded response generation.
• Develop reusable prompt templates, prompt versions, context builders, response schemas, evaluation routines and AI orchestration services.
• Integrate with enterprise AI services such as Azure OpenAI, Azure AI Foundry, OpenAI APIs, Google Gemini, Anthropic, Hugging Face or equivalent approved platforms.
• Implement AI run logging, prompt/model metadata capture, evidence citations, output traceability, reviewer feedback capture and human-in-the-loop controls.
• Build AI evaluation routines for answer quality, retrieval quality, hallucination checks, regression testing, consistency and groundedness.
• Collaborate with backend and DevOps teams to containerize AI services, deploy them securely, monitor usage, track costs and troubleshoot production issues.
• Support responsible AI practices such as prompt injection checks, data leakage prevention, policy-based guardrails and AI output validation.

Additional Responsibilities:

• Experience with Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure Document Intelligence, Google AI Studio/Gemini, AWS Bedrock or Vertex AI.
• Exposure to RAG evaluation tools such as RAGAS, DeepEval, Promptfoo, LangSmith or equivalent frameworks.
• Experience with AI governance, prompt/model registry, AI audit logs, explainability, groundedness checks and human review workflows.

Technical and Professional Requirements:

• Minimum 7-10 years of experience in software engineering, AI/ML engineering, applied ML, data science engineering or related roles.
• Strong hands-on Python programming experience and practical exposure to LLM-based application development.
• Experience with RAG, vector databases, embeddings, prompt engineering, evaluation frameworks and AI service integration.
• Experience with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI or equivalent tools.
• Working knowledge of REST APIs, microservices, SQL, structured data concepts, Git workflows, testing and software engineering practices.
• Understanding of document extraction, semantic search, NLP, retrieval quality, hallucination risk, prompt safety and AI evaluation methods.
• Ability to build production-oriented AI components rather than isolated proof-of-concept demos.

Preferred Skills:

• Technology->AI-Data science->Amazon ML
• Technology->AI-Data science->PYTHON
• Technology->Enterprise Architecture->Digital Architecture
• Technology->Enterprise Architecture->API / Microservices Architecture
• Technology->Cloud Platform->Azure Networking Services->- Azure Bastion
• Technology->AI-Generative AI->Generative AI - Basic->retrieval augmented generation (rag)
• Technology->AI-AI Engineering->AI/ML Solution Architecture and Design->traditional ai ml
• Technology->AI-AI Engineering->LLMOps