Technology Architect - AI/GenAI-Q2 FY 27

Infosys; Bengaluru; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Infosys is looking for a skilled candidate in Bengaluru, India to design and develop enterprise-grade Generative AI solutions using Python and cutting-edge AI frameworks.

The ideal candidate will create AI applications utilizing LLMs and RAG pipelines, manage cloud deployments on platforms like Azure, AWS, or GCP, and employ best practices for model engineering to ensure scalable and reliable AI systems.

Responsibilities

• Design and develop enterprise-grade Generative AI solutions using Python and modern AI frameworks.
• Build AI applications using LLMs, embeddings, prompts, and tool usage.
• Implement RAG pipelines for knowledge retrieval and generation.
• Design end-to-end AI solutions covering Data ingestion, Data processing, Retrieval, Generation, Evaluation.
• Support LLM fine-tuning workflows including instruction tuning.

Skills

Generative AI solutions design

Python

AI frameworks

Retrieval-Augmented Generation (RAG)

Docker

Kubernetes

Education

Bachelor of Engineering

Tools

Cloud platforms (Azure, AWS, GCP)

LangChain

LangGraph

LlamaIndex

AutoGen

Langfuse

Job description

Educational Requirements

Bachelor of Engineering

Service Line

Strategic Technology Group

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.

Preferred Skills

• Technology->AI-Responsible AI->Responsible AI->explainable ai
• Technology->AI-AI Engineering->LLMOps