Databricks+ Gen AI

Infosys; Hyderabad, Chennai District, Bengaluru; Hybrid; INR 1,800,000 - 3,800,000; Full time

Job summary

Infosys is seeking a Generative AI Engineer with hands-on experience in Databricks and a strong Data Engineering background. You will build AI-driven applications using LLMs, Retrieval-Augmented Generation (RAG), and scalable data pipelines.

The role involves designing, deploying, and optimizing AI solutions on cloud platforms, developing PySpark/Python workflows, and collaborating with data teams to deliver production-ready capabilities.

Qualifications

• 2–5 years of hands-on Generative AI experience, with Databricks.
• Strong background in Data Engineering and building data pipelines.
• Experience with LLMs, RAG, and model deployment best practices.

Responsibilities

• Design, develop, and deploy Generative AI applications on Databricks and cloud platforms.
• Build and optimize RAG-based solutions using vector databases and enterprise data sources.
• Develop scalable data pipelines using Databricks, PySpark, and Python.

Skills

Generative AI

LLMs

RAG

Data Engineering

APIs

Tools

Databricks

PySpark

Python

Cloud platforms

Job description

Role: Generative AI Engineer Databricks
Experience: 2-5 Years
Employment Type: Full-Time

Job Summary:

We are looking for a Generative AI Engineer with hands‑on experience in Databricks and a strong background in Data Engineering. The ideal candidate should have expertise in building AI‑driven applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and scalable data pipelines. Experience working with Databricks, PySpark, cloud platforms, and modern AI frameworks is highly preferred.

Key Responsibilities:

• Design, develop, and deploy Generative AI applications using Databricks and cloud platforms.
• Build and optimize RAG-based solutions leveraging vector databases and enterprise data sources.
• Develop scalable data pipelines using Databricks, PySpark, and Python.
• Fine‑tune, evaluate, and integrate LLMs into business applications.
• Work with structured and unstructured datasets to support AI/ML use cases.
• Implement prompt engineering, model evaluation, and AI governance best practices.
• Develop APIs and microservices for AI model integration.
• Collaborate with Data Engineers, Data Scientists, and Business stakeholders to deliver AI solutions.
• Monitor model performance, latency, and scalability in production environments.
• Follow MLOps best practices for model deployment, experimentation, and monitoring.