AI Data Engineer

ExlService Holdings, Inc.; Dadri; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

ExlService Holdings, Inc. is seeking an experienced AI Engineer in India to design and deploy GenAI/LLM-powered applications.

You will build RAG pipelines, implement prompt engineering, and develop backend services with Python frameworks while ensuring data quality and robust deployment practices. Responsibilities include collaborating with Data Engineering and MLOps teams, applying guardrails, and documenting reusable components for enterprise AI solutions.

Qualifications

• 4–6 years total experience in software/data engineering.
• 1+ year hands-on experience in GenAI / LLM-based applications.
• Experience with building and deploying LLM-based solutions.
• Strong Python/PySpark production-grade development experience.
• Experience with data ingestion, preprocessing, and API integration.

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 applications using Python frameworks (FastAPI/Flask/Streamlit).
• Integrate AI solutions with enterprise systems, databases, and APIs.
• Apply guardrails and validation checks to improve quality and reduce hallucinations.
• Collaborate with Data Engineering for data quality and documentation.
• Partner with MLOps for deployment, monitoring, and iterative improvements.
• Document solutions, reusable components, and best practices.

Skills

GenAI development

RAG pipelines

LangChain / LangGraph

Agent orchestration

Python / PySpark

Data engineering

CI/CD / containers

Tools

LangChain

LangGraph

Azure Databricks

Snowflake

Cloud platforms (Azure/AWS/GCP)

APIs integration

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