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Senior AI Data Engineer

Reads like an AI Engineer role: building products on existing AI models.

Pay
Pay not listed
Work setting
Hybrid — work setting source
Work setup in the employer’s posting: Hybrid
From the employer’s posting
Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting

What you’ll work on

Full posting
  • Design and develop LLM-powered applications using single or multi-agent patterns

  • Build and optimise end-to-end RAG pipelines

  • Develop production-grade APIs and services for GenAI applications

  • Integrate LLM solutions with enterprise systems, data platforms and workflows

  • Apply guardrails and evaluation frameworks to reduce hallucinations

From the employer’s posting
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)
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
Apply at EXL Service

Posting record

Report an error

Open 3 monthsWe first saw this posting 93 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.

Status in our records
Live
First seen by us
Aug 12, 202693 days ago
Recorded sightings
346
Last seen by us
Nov 13, 2026
Employer says posted
Jul 8, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 93

Up longer than most. Similar jobs usually come down between day 4 and day 77.

Well past EXL Service’s usual: its jobs come down by about day 21.

From 51 similar postings that came down, and 281 of EXL Service’s. Coming down is not always a hire.

EXL Service is hiring

211 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Takes jobs down at a usual pace
  • 5% of open jobs were reposted
Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • SQL
  • AWS
  • Azure
  • Databricks
  • GCP
  • Snowflake
  • FastAPI
  • Flask
  • PySpark
  • Streamlit
Where the posting names them
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
- Cloud platforms (Azure/AWS/GCP) - SQL - Containers, CI/CD, monitoring
- Good exposure to: - Cloud platforms (Azure/AWS/GCP) - SQL
- Deep data analysis experience and handling large volume of data - Fabric/Azure Databricks/Snowflake data engineering integration skills - Good exposure to:
- 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)
- 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

Your next step

Complete your application on fa-ewjt-saasfaprod1.fa.ocs.oraclecloud.com. The employer’s form will show what is required.

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Already applied? Track this application

Has EXL Service posted this job before?

Yes. We’ve followed one other Senior AI Data Engineer job there since August 2026. It is still up.

  1. GurugramStill up after 93 days, first seen Aug 12
  2. This jobUp 93 days so far, first seen Aug 12

About EXL Service

People
About 65,000 in 2025, up 9% from the year before.Annual report (SEC Form 10-K)
Company
Listed as EXLS.

Demand for AI Engineer roles

New AI Engineer postings at the employers we follow are up 134% over the last 6 weeks compared with the 6 before.

Weekly new AI Engineer postings at the same employers, from the week of August 17 to the week of November 2. The fewest in a week was 7, the most 65, and the latest week had 65.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 6 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under AI Engineer, by the work the posting describes.

AI engineers build products on top of AI models that already exist, most often large language models: they connect a model to a company's data and tools and ship the assistants, agents and search features people use.

Postings ask for strong Python or TypeScript, experience with model APIs, retrieval and agent frameworks, and ways to test whether the model's answers are good enough.

An ML engineer trains and serves models; an AI engineer usually starts from a model someone else trained and builds the product around it, though titles often mix the two.

Job description

View original posting ↗

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)

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)

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)
Source details

View original posting ↗

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Noida, Uttar Pradesh, India

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