AI Data Engineer

EXL; Maharashtra; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

EXL in India is seeking a seasoned GenAI/LLM engineer to design and develop LLM-based applications for business use cases. You will build RAG pipelines, implement prompt engineering, and develop backend APIs using Python frameworks such as FastAPI or Flask, integrating AI solutions with enterprise systems and databases.

You will collaborate with Data Engineering and MLOps teams for deployment, monitoring, and continuous improvement, while documenting solutions and reusable components for

Qualifications

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

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/GenAI

RAG pipelines

Agentic AI

LangChain/LangGraph

Tool-calling

Python

Pyspark

API integration

SQL

CI/CD

Tools

LangChain

Azure Databricks

Snowflake

FastAPI

Flask

Streamlit

Python

Pyspark

Job description

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

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