AI/ML Engineer - DaAI

Infosys; Bengaluru; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Infosys is seeking a skilled AI/ML engineer in Bengaluru to design and implement AI agents, retrieval-augmented generation pipelines, and enterprise data understanding. The role focuses on building scalable AI workflows, integrating with LLM APIs, and evaluating system performance.

You will work with Python, REST APIs, and modern software practices to deliver robust AI solutions across structured data, documents, and knowledge discovery workflows.

Qualifications

• Strong hands-on programming experience in Python and building AI agents.
• Practical exposure to LLMs, RAG, vector databases, embedding models, prompt engineering, and AI application development.
• Experience with AI/LLM frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, or equivalent tools.
• Working knowledge of REST APIs, microservices, Git-based development, unit testing, and modern software engineering practices.
• Familiarity with SQL and structured data concepts, including tables, schemas, joins, relationships, and query generation.

Responsibilities

• Build and enhance AI agent workflows using LangGraph, LangChain, AutoGen, CrewAI, or equivalent technologies.
• Implement AI agents for data discovery, profiling, enrichment, extraction, classification, contextual reasoning, and enterprise knowledge discovery.
• Develop retrieval-augmented generation pipelines, including document chunking, embedding generation, metadata tagging, vector indexing, retrieval tuning, and response generation.
• Work with vector databases such as Pinecone, Milvus, Qdrant, Weaviate, pgvector, Chroma, or equivalent technologies.
• Build structured data agents that can connect to databases, inspect schemas, generate SQL, and support semantic understanding of enterprise data.
• Implement document intelligence workflows for PDFs, Word documents, emails, transcripts, logs, and semi-structured enterprise content using Azure Document Intelligence, AWS Textract, LlamaParse, or equivalent tools.
• Implement AI evaluation routines covering answer quality, retrieval quality, hallucination checks, regression tests, robustness checks, and response consistency.
• Support integration with LLM APIs and open-source models from providers such as OpenAI, Anthropic, Azure OpenAI, Hugging Face, Llama, Gemma, or equivalent ecosystems.

Skills

Python

AI agents

LLMs

RAG

Vector databases

Embedding models

Prompt engineering

AI application development

REST APIs

Git

Unit testing

Software engineering

SQL

Document intelligence

Tools

LangChain

LangGraph

LlamaIndex

AutoGen

CrewAI

Semantic Kernel

Pinecone

Milvus

Qdrant

Weaviate

pgvector

Chroma

Azure Document Intelligence

AWS Textract

LlamaParse

Job description

• Strong hands-on programming experience in Python and building AI agents.
• Practical exposure to LLMs, RAG, vector databases, embedding models, prompt engineering, and AI application development.
• Experience with one or more AI/LLM frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, or equivalent tools.
• Working knowledge of REST APIs, microservices, Git-based development, unit testing, and modern software engineering practices.
• Familiarity with SQL and structured data concepts, including tables, schemas, joins, relationships, and query generation.
• Build and enhance AI agent workflows using frameworks such as LangGraph, LangChain, AutoGen, CrewAI, or equivalent technologies.
• Implement AI agents for data discovery, profiling, enrichment, extraction, classification, contextual reasoning, and enterprise knowledge discovery.
• Develop retrieval-augmented generation pipelines, including document chunking, embedding generation, metadata tagging, vector indexing, retrieval tuning, and response generation.
• Work with vector databases such as Pinecone, Milvus, Qdrant, Weaviate, pgvector, Chroma, or equivalent technologies.
• Build structured data agents that can connect to databases, inspect schemas, generate SQL, and support semantic understanding of enterprise data.
• Implement document intelligence workflows for PDFs, Word documents, emails, transcripts, logs, and semi-structured enterprise content using Azure Document Intelligence, AWS Textract, LlamaParse, or equivalent tools.
• Implement AI evaluation routines covering answer quality, retrieval quality, hallucination checks, regression tests, robustness checks, and response consistency.
• Support integration with LLM APIs and open-source models from providers such as OpenAI, Anthropic, Azure OpenAI, Hugging Face, Llama, Gemma, or equivalent ecosystems.