AI Data Engineer

EXL Talent Acquisition Team; Dadri; On-site; INR 1,500,000 - 2,200,000; Full time

Job summary

EXL Talent Acquisition Team is seeking a skilled AI Engineer to design and build LLM-based applications in an enterprise setting. You will own end-to-end development of RAG pipelines, implement prompt engineering, and deploy backend services using FastAPI/Flask/Streamlit.

The role emphasizes data quality, security, and scalable AI solutions. Ideal candidates have 4–6 years total experience with at least 1 year in GenAI/LLM projects, and are proficient with LangChain/LangGraph, Python, and cloud

Qualifications

• 4–6 years total experience, with 1+ year hands-on GenAI/LLM-based apps experience.
• Experience building LLM-based applications and agentic AI.
• Proficient with LangChain or LangGraph and tool-calling Architectures.
• Strong Python/PySpark production-grade development and API integration experience.
• Data engineering basics: ETL/ELT, pipelines, orchestration; analytics mindset.
• Familiarity with cloud platforms (Azure/AWS/GCP) and data privacy considerations.

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 (templates, chaining, basic tool calls).
• 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 reliability and reduce hallucination.
• Collaborate with Data Engineering for data quality and pipeline efficiency; document components.
• Work with MLOps for deployment, monitoring, and iterative improvements; document best practices.

Skills

GenAI

LLM Apps

RAG pipelines

Prompt engineering

Python

FastAPI

API integration

MLOps

Data quality

Documentation

Tools

LangChain

LangGraph

Azure Databricks

Snowflake

CI/CD

Containers

REST APIs

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