AI Data Engineer

EXL Talent Acquisition Team; Pune District; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

EXL Talent Acquisition Team in Pune seeks a seasoned AI engineer to lead GenAI and agentic AI initiatives. You will design and deploy LLM-based applications, build robust RAG pipelines, and craft effective prompts to deliver enterprise-grade results.

You will collaborate with data engineers, ML engineers, and MLOps to ensure scalable backends, data quality, and reliable deployments for real client-use AI solutions.

Qualifications

• 4–6 years total experience with 1+ year hands-on GenAI/LLM-based applications.
• Experience designing and implementing LLM-powered automation and AI pipelines.
• Strong understanding of RAG pipelines, prompt templates, and tool-calling.
• Hands-on coding in Python and API integration for AI services.

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

Skills

LLM-based development

RAG pipelines

prompt engineering

Python

API integration

LangChain

Agent orchestration

Data quality

Tools

LangChain

LangGraph

Azure Databricks

Snowflake

FastAPI

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