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EXL Service

AI Data Engineer

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

Noida, Uttar Pradesh, India

Open 3 months, first seen by us 93 days ago
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-based applications using single-agent or simple multi-agent patterns

  • Develop backend APIs for AI applications with FastAPI, Flask, or Streamlit

  • Integrate AI solutions with enterprise systems, databases, and APIs

  • Apply guardrails and validation checks to improve response quality and reduce hallucination

From the employer’s posting
Design and develop LLM-based applications using single-agent or simple multi-agent patterns for business use cases
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

What you’ll bring

All qualifications

Core experience

  • 4–6 years total experience, with 1+ year hands-on experience in GenAI / LLM-based applications
  • Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
  • Prior experience in one or more:
  • Experience with evaluation of LLM outputs (quality, relevance, latency)
  • Experience working on real client-facing AI solutions or POCs
  • 4–6 years total experience, with 1+ year hands-on experience in GenAI / LLM-based applications
Qualification wording
4–6 years total experience, with 1+ year hands-on experience in GenAI / LLM-based applications
Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
Prior experience in one or more:
Experience with evaluation of LLM outputs (quality, relevance, latency)
Experience working on real client-facing AI solutions or POCs
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
- 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
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:
- 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
Upload resumeAdd LinkedIn

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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Has EXL Service posted this job before?

Yes. We’ve followed 2 other AI Data Engineer jobs there since August 2026. All 2 are still up.

  1. GurugramStill up after 93 days, first seen Aug 12
  2. PuneStill up after 93 days, first seen Aug 12
  3. 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-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

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

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
Source details

View original posting ↗

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Pay

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Location & working pattern

Noida, Uttar Pradesh, India

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Open source at fa-ewjt-saasfaprod1.fa.ocs.oraclecloud.com ↗Read the full description ↓

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