Python Developer - Agentic AI / LLM / RAG Engineer

Infosys; Bengaluru; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Infosys in Bengaluru seeks an AI engineer to develop and maintain AI applications using Python and to design Agentic AI systems. You will Build and optimize RAG pipelines with vector databases and integrate multiple LLMs.

The role requires hands-on experience with LangChain/LangGraph technologies and cloud deployment for scalable AI solutions. The candidate should be adept at API development, prompt engineering, and AI governance practices while collaborating with cross-functional teams to

Qualifications

• Strong programming experience in Python.
• Hands-on experience with LLMs, Generative AI, Agentic AI, and RAG architectures.
• Experience with LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, or equivalent frameworks.
• Knowledge of vector databases such as Pinecone, ChromaDB, Weaviate, FAISS, or Azure AI Search.
• Experience with REST APIs, FastAPI, Flask, or Django.
• Understanding of embeddings, semantic search, prompt engineering, and model evaluation.
• Experience with Git, CI/CD, and software development best practices.
• Knowledge of cloud AI services (Azure OpenAI preferred).
• Experience with multi-agent architectures and AI orchestration.
• Knowledge of MLOps, model deployment, and containerization (Docker/Kubernetes).
• Exposure to NLP, machine learning, and deep learning concepts.
• Experience with enterprise AI governance, security, and responsible AI practices.

Responsibilities

• Develop and maintain AI applications using Python.
• Design and implement Agentic AI systems using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar.
• Build and optimize RAG pipelines using vector databases and retrieval frameworks.
• Integrate and fine-tune Large Language Models (LLMs) including OpenAI, Azure OpenAI, Llama, Claude, Gemini, or similar.
• Develop APIs and microservices for AI applications.
• Implement prompt engineering, workflow orchestration, and AI agent collaboration patterns.
• Create scalable solutions using cloud platforms such as Azure, AWS, or GCP.
• Monitor, evaluate, and improve AI model performance, accuracy, and response quality.
• Collaborate with business and technology teams to translate requirements into AI-driven solutions.

Skills

Python

LLMs

Generative AI

Agentic AI

RAG architectures

LangChain

LangGraph

CrewAI

AutoGen

LlamaIndex

Pinecone

ChromaDB

Weaviate

FAISS

Azure AI Search

REST APIs

FastAPI

Flask

Django

Embeddings

Semantic search

Prompt engineering

Model evaluation

Git

CI/CD

Best practices

Azure OpenAI

AI governance

Security

Responsible AI

MLOps

Model deployment

Docker

Kubernetes

NLP

Machine learning

Deep learning

Job description

Required Skills

• Strong programming experience in Python.
• Hands-on experience with LLMs, Generative AI, Agentic AI, and RAG architectures.
• Experience with LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, or equivalent frameworks.
• Knowledge of vector databases such as Pinecone, ChromaDB, Weaviate, FAISS, or Azure AI Search.
• Experience with REST APIs, FastAPI, Flask, or Django.
• Understanding of embeddings, semantic search, prompt engineering, and model evaluation.
• Experience with Git, CI/CD, and software development best practices.
• Knowledge of cloud AI services (Azure OpenAI preferred).
• Experience with multi-agent architectures and AI orchestration.
• Knowledge of MLOps, model deployment, and containerization (Docker/Kubernetes).
• Exposure to NLP, machine learning, and deep learning concepts.
• Experience with enterprise AI governance, security, and responsible AI practices.

Key Responsibilities

• Develop and maintain AI applications using Python.
• Design and implement Agentic AI systems using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar.
• Build and optimize RAG pipelines using vector databases and retrieval frameworks.
• Integrate and fine-tune Large Language Models (LLMs) including OpenAI, Azure OpenAI, Llama, Claude, Gemini, or similar.
• Develop APIs and microservices for AI applications.
• Implement prompt engineering, workflow orchestration, and AI agent collaboration patterns.
• Create scalable solutions using cloud platforms such as Azure, AWS, or GCP.
• Monitor, evaluate, and improve AI model performance, accuracy, and response quality.
• Collaborate with business and technology teams to translate requirements into AI-driven solutions.

Technology Stack Technology | Artificial Intelligence & Automation Skill Category | Python Development, Agentic AI, LLM, RAG, Generative AI Primary Skills | Python, LangChain, LangGraph, CrewAI, Azure OpenAI, RAG, Vector Databases Secondary Skills | FastAPI, Docker, Kubernetes, Azure AI Search, MLOps