Senior Technologist - AI/GenAI/Python-Q3 FY 27

Infosys; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Infosys in Bengaluru seeks a highly skilled Generative AI Engineer / Senior Technologist with hands-on Python, LLMs, RAG, and agentic AI experience to design, build, deploy and evaluate enterprise AI solutions.

You will work across data ingestion, processing, retrieval, generation, and evaluation, apply prompt engineering, fine-tuning decisions, and deploy workloads on Azure/AWS/GCP, using Docker and Kubernetes.

Qualifications

• Experience designing enterprise-grade AI solutions with Python and modern frameworks.
• Experience with LLMs, embeddings, prompts, function calling and agentic AI patterns.
• Experience deploying AI workloads on Azure, AWS, or GCP and containerizing with Docker.
• Knowledge of Retrieval-Augmented Generation (RAG) pipelines and model fine-tuning considerations.

Responsibilities

• Design and develop enterprise-grade Generative AI solutions using Python and modern AI frameworks.
• Build AI applications using LLMs, embeddings, prompts, function calling, tool usage, and agentic AI patterns.
• Implement Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge retrieval and generation use cases.
• Design end-to-end AI solutions covering: data ingestion, data processing, retrieval, generation, evaluation.
• Work with Agentic AI frameworks such as CrewAI, LangChain, LangGraph, LlamaIndex, AutoGen, and Langfuse.
• Apply prompt engineering strategies to improve LLM accuracy, reliability, and usability.
• Evaluate the suitability of fine-tuning vs RAG based on business and technical requirements.
• Support LLM fine-tuning workflows including instruction tuning and supervised fine-tuning.
• Manage data preparation for fine-tuning, including train/validation splits, data quality checks, and contamination prevention.
• Deploy AI workloads on cloud platforms such as Azure, AWS, or GCP.
• Containerize AI applications using Docker and work with basic Kubernetes concepts.
• Apply model engineering best practices for scalable, reliable, and production-ready AI systems.

Skills

Python

LLMs

RAG

Agentic AI

Fine-tuning

Cloud deployment

Prompt engineering

Evaluation

System design

Education

Bachelor of Engineering

Tools

CrewAI

LangChain

LangGraph

LlamaIndex

AutoGen

Langfuse

Docker

Kubernetes

Azure

AWS

GCP

Job description

Job Description

We are looking for a highly skilled Generative AI Engineer / Senior Technologist with strong hands-on experience in Python, Large Language Models, RAG, Agentic AI, fine-tuning, and cloud deployment. The ideal candidate should have a deep understanding of LLM architectures at a systems level and the ability to design, build, deploy, and evaluate end-to-end AI solutions for enterprise use cases.

This role requires strong engineering capability, problem-solving skills, and practical exposure to modern AI frameworks, cloud-native deployment, and model engineering concepts.

Roles Responsibilities

• Design and develop enterprise-grade Generative AI solutions using Python and modern AI frameworks.
• Build AI applications using LLMs, embeddings, prompts, function calling, tool usage, and agentic AI patterns.
• Implement Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge retrieval and generation use cases.
• Design end-to-end AI solutions covering:
  • o Data ingestion
  • o Data processing
  • o Retrieval
  • o Generation
  • o Evaluation
• Work with Agentic AI frameworks such as CrewAI, LangChain, LangGraph, LlamaIndex, AutoGen, and Langfuse.
• Apply prompt engineering strategies to improve LLM accuracy, reliability, and usability.
• Evaluate the suitability of fine-tuning vs RAG based on business and technical requirements.
• Support LLM fine-tuning workflows including instruction tuning and supervised fine-tuning.
• Manage data preparation for fine-tuning, including train/validation splits, data quality checks, and contamination prevention.
• Deploy AI workloads on cloud platforms such as Azure, AWS, or GCP.
• Containerize AI applications using Docker and work with basic Kubernetes concepts.
• Apply model engineering best practices for scalable, reliable, and production-ready AI systems.

Educational Requirement

Bachelor of Engineering

Preferred Skills

Technology->Open System->Open System- ALL,Technology->AI-AI Engineering->AI/ML Solution Architecture and Design->traditional ai ml,Technology->OpenSystem->Python - OpenSystem

Service Line

Strategic Technology Group