GenAI / LLM Engineer

Capgemini; Hyderabad, Bengaluru, Mumbai; Hybrid; INR 1,500,000 - 3,000,000; Full time

Job summary

Capgemini is seeking a GenAI Engineer to design and develop scalable AI solutions using Python, LLMs, RAG, and modern data platforms. You will build RAG pipelines, design ETL workflows, and optimize vector databases for AI use cases.

The role requires expertise in LangChain/LlamaIndex, cloud services (AWS/Azure/GCP), and MLOps/LLMOps to support model deployment, monitoring, and cross-functional collaboration.

Qualifications

• Proficient Python programming with hands-on GenAI experience.
• Solid understanding of LLM-based architectures and RAG techniques.
• Experience building scalable AI solutions on cloud platforms.

Responsibilities

• Develop and deploy GenAI and LLM-based applications.
• Build RAG pipelines using LangChain and LlamaIndex.
• Design ETL workflows and data engineering solutions.
• Integrate and optimize vector databases for AI use cases.
• Apply MLOps/LLMOps practices for deployment and monitoring.
• Collaborate with cross-functional teams to deliver AI-driven solutions.

Skills

Python

GenAI

LLMs

RAG

LangChain

LlamaIndex

SQL

Data Engineering

ETL

Vector Databases

Cloud (AWS/Azure/GCP)

MLOps/LLMOps

Tools

LangChain

LlamaIndex

Pinecone

FAISS

ChromaDB

Milvus

Job description

Job Summary:

We are looking for a skilled GenAI Engineer with expertise in Python, LLMs, RAG, LangChain/LlamaIndex, Data Engineering, and Cloud technologies. The ideal candidate will design and develop scalable AI solutions leveraging Generative AI and modern data platforms.

Key Responsibilities:

• Develop and deploy GenAI and LLM-based applications.


• Build RAG pipelines using LangChain and LlamaIndex.


• Design ETL workflows and data engineering solutions.


• Integrate and optimize Vector Databases for AI use cases.


• Implement MLOps/LLMOps practices for model deployment and monitoring.


• Collaborate with cross-functional teams to deliver AI-driven solutions.

Required Skills:

• Python


• Generative AI (GenAI)


• Large Language Models (LLMs)


• RAG (Retrieval-Augmented Generation)


• LangChain / LlamaIndex


• SQL


• Data Engineering & ETL


• Vector Databases (Pinecone, FAISS, ChromaDB, Milvus, etc.)


• AWS, Azure, or GCP


• MLOps / LLMOps