Senior AI Data Engineer

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

Job summary

EXL Talent Acquisition Team in India seeks an experienced GenAI/LLM engineer to design and deliver LLM-powered applications with agentic patterns and robust RAG pipelines. You will build production-grade APIs, ensure guardrails, and collaborate with Data Engineering and MLOps to scale deployments.

The role requires 6–9 years total experience and 1–3+ years in hands-on GenAI/LLM development, with deep knowledge of LangChain, LangGraph, Python, and cloud platforms.

Qualifications

• Hands-on GenAI/LLM development and production use cases.
• Experience with RAG pipelines and retrieval optimisation.
• Strong API development and data handling skills.

Responsibilities

• Design and develop LLM-powered applications using agentic patterns.
• Build end-to-end RAG pipelines including ingestion and retrieval.
• Implement prompt engineering and orchestration techniques.
• Develop production-grade APIs/services for GenAI apps.
• Integrate LLM solutions with enterprise data platforms and workflows.
• Apply guardrails and evaluation frameworks to improve quality.
• Collaborate with Data Eng and MLOps for pipelines and deployment.
• Contribute reusable components and documentation.

Skills

GenAI development

LLM integration

Python

Pyspark

API development

Data analysis

Cloud platforms

Tools

LangChain

LangGraph

Azure Databricks

Snowflake

Docker / CI/CD

OpenAI / Claude

Tool-calling architectures

Job description

Key Responsibilities

• Design and develop LLM-powered applications using agentic patterns (single/multi-agent) for business use cases
• Build and optimise end-to-end RAG pipelines (ingestion, embeddings, retrieval, orchestration, response synthesis)
• Implement prompt engineering and orchestration techniques (prompt chaining, tool/function calling, structured outputs)
• Develop production-grade APIs and services (FastAPI/Flask/Streamlit) for GenAI applications
• Integrate LLM solutions with enterprise systems, data platforms, and workflows
• Apply guardrails and evaluation frameworks to improve response quality, reduce hallucinations, and ensure responsible AI usage
• Collaborate with Data Engineering and MLOps teams for data pipelines, deployment, monitoring, and scaling
• Contribute to reusable components, documentation, and engineering best practices

Experience & Core Requirements (Must-Have)

Overall Experience

• 6–9 years total experience
• 1–3+ years in hands-on GenAI / LLM application development (production use cases)

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

• Experience with fine-tuning techniques (LoRA, PEFT) or prompt tuning strategies
• Experience with enterprise GenAI security & privacy practices (data masking, access control, compliance)
• Familiarity with Azure AI ecosystem (Azure OpenAI, Azure AI Search, Fabric, etc.)

Exposure to agentic coding tools (e.g., Claude Code or similar environments)