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Salary
≈ $18k – $41k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Senior

Confirmed on the employer's own hiring board on Oct 19, 2026. First seen by Alion on Oct 6, 2026. Infosys scores A on the Alion truth index.

Overview
Company
Impact
Profile match
Infosys Limited is an Indian multinational information technology company providing business consulting, information technology, and outsourcing services. Founded in 1981 by N. R. Narayana Murthy and six engineers in Pune, India, the company is headquartered in Bengaluru and stands as one of India's largest IT services companies.

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.

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.

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

Education

Bachelor of Engineering

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