AI Data Engineer

EXL Talent Acquisition Team; Gurugram District; On-site; INR 850,000 - 1,200,000; Full time

Job summary

EXL Talent Acquisition Team is seeking a seasoned AI engineer to design and deliver LLM-based applications in enterprise settings, focusing on reliability and performance. You will build and optimize RAG pipelines, implement prompt engineering, and develop backend services using Python.

Collaborate with Data Engineering and MLOps teams to ensure data quality, deployment, monitoring, and documentation. Experience with LangChain, Azure/AWS/GCP, and data privacy considerations is a plus.

Qualifications

• 4–6 years of total experience with 1+ year hands-on GenAI/LLM work.
• Hands-on experience building production-grade AI applications.
• Strong Python and API integration skills.

Responsibilities

• Design and develop LLM-based applications using single-agent or simple multi-agent patterns.
• Build and maintain RAG pipelines: data ingestion → chunking → embeddings → retrieval → response generation
• Implement prompt engineering techniques (templates, chaining, tool calling)
• Develop backend services/APIs for AI apps using Python frameworks (FastAPI / Flask / Streamlit)
• Integrate AI solutions with enterprise systems, databases, and APIs
• Apply guardrails and validation checks to improve quality
• Collaborate with Data Eng and MLOps for data quality, deployment, monitoring, and documentation
• Document solutions, reusable components, and best practices

Skills

LLMs / GenAI

RAG pipelines

Agentic AI

LangChain / LangGraph

Agent orchestration

LLM evaluation & optimization

Python

Pyspark

API integration

Tools

Azure Databricks

Snowflake

CI/CD tooling

Cloud platforms (Azure/AWS/GCP)

Job description

Key Responsibilities

• Design and develop LLM-based applications using single-agent or simple multi-agent patterns for business use cases


• Build and maintain RAG pipelines: data ingestion → chunking → embeddings → retrieval → response generation


• Implement prompt engineering techniques (prompt templates, chaining, basic tool/function calling)


• Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit)


• Integrate AI solutions with enterprise systems, databases, and APIs


• Apply basic guardrails and validation checks to improve response quality and reduce hallucination


• Work with Data Engineering teams to ensure data quality, pipeline efficiency, and proper documentation


• Collaborate with MLOps teams for deployment, monitoring, and iterative improvements


• Document solutions, reusable components, and best practices

Must-Have Skills

Experience

• 4–6 years total experience, with 1+ year hands-on experience in GenAI / LLM-based applications

LLM / GenAI & Agentic Engineering

• Strong hands-on experience with:
  
  
  • LLMs (Claude, OpenAI, etc.)
  
  
  • RAG pipelines and retrieval optimisation
  
  
  • GPT + Agentic AI implementation experience
  
  


• Experience with:
  
  
  • LangChain, LangGraph, or similar frameworks
  
  
  • Agent orchestration and tool-calling architectures
  
  


• Deep understanding of:
  
  
  • LLM limitations, evaluation, and optimisation strategies
  
  

Core Engineering

• Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience


• Deep data analysis experience and handling large volume of data


• Fabric/Azure Databricks/Snowflake data engineering integration skills


• Good exposure to:
  
  
  • Cloud platforms (Azure/AWS/GCP)
  
  
  • SQL
  
  
  • Containers, CI/CD, monitoring
  
  

Data / AI Foundations (Mandatory)

Prior experience in one or more:

• Data Engineering (ETL/ELT, pipelines, orchestration)


• Data Science / ML lifecycle (especially NLP)


• Analytics engineering / data products

Good-to-Have / Preferred

• Exposure to model fine-tuning (LoRA/PEFT) or prompt optimisation techniques


• Experience with evaluation of LLM outputs (quality, relevance, latency)


• Understanding of enterprise data privacy and security considerations in GenAI


• Exposure to Azure AI / Azure OpenAI / AI Search ecosystems


• Experience working on real client-facing AI solutions or POCs