AI Data Engineer

Pune, Maharashtra, India

1+ years exp

Full-time

Posted 5d ago

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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 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 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 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 Requirements & Skills

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

Frequently Asked Questions

How to apply for AI Data Engineer at EXL?

Click the "Apply on Company Website" button on this page to submit your application directly on the employer's official portal.

What is the salary for this role?

Salary details will be discussed during the interview.

What experience is required?

1+ years of experience is required.

Is this position still open?

Yes, currently active and accepting applications.

AI Data Engineer

Check Your Resume Match Score

Apply for this position

Job Description

Key Responsibilities

LLM-based applications

prompt engineering techniques

Python frameworks (FastAPI / Flask / Streamlit)

guardrails and validation checks

data quality, pipeline efficiency, and proper documentation

deployment, monitoring, and iterative improvements

Must-Have Skills

Experience

4–6 years total experience

1+ year hands-on experience in GenAI / LLM-based applications

LLM / GenAI & Agentic Engineering

Core Engineering

Data / AI Foundations (Mandatory)

Good-to-Have / Preferred

model fine-tuning (LoRA/PEFT) or prompt optimisation techniques

evaluation of LLM outputs (quality, relevance, latency)

enterprise data privacy and security considerations in GenAI

Azure AI / Azure OpenAI / AI Search ecosystems

real client-facing AI solutions or POCs

Key Responsibilities

LLM-based applications

prompt engineering techniques

Python frameworks (FastAPI / Flask / Streamlit)

guardrails and validation checks

data quality, pipeline efficiency, and proper documentation

deployment, monitoring, and iterative improvements

Must-Have Skills

Experience

4–6 years total experience

1+ year hands-on experience in GenAI / LLM-based applications

LLM / GenAI & Agentic Engineering

Core Engineering

Data / AI Foundations (Mandatory)

Good-to-Have / Preferred

model fine-tuning (LoRA/PEFT) or prompt optimisation techniques

evaluation of LLM outputs (quality, relevance, latency)

enterprise data privacy and security considerations in GenAI

Azure AI / Azure OpenAI / AI Search ecosystems

real client-facing AI solutions or POCs

Key Responsibilities

LLM-based applications

prompt engineering techniques

Python frameworks (FastAPI / Flask / Streamlit)

guardrails and validation checks

data quality, pipeline efficiency, and proper documentation

deployment, monitoring, and iterative improvements

Must-Have Skills

Experience

4–6 years total experience

1+ year hands-on experience in GenAI / LLM-based applications

LLM / GenAI & Agentic Engineering

Core Engineering

Data / AI Foundations (Mandatory)

Good-to-Have / Preferred

model fine-tuning (LoRA/PEFT) or prompt optimisation techniques

evaluation of LLM outputs (quality, relevance, latency)

enterprise data privacy and security considerations in GenAI

Azure AI / Azure OpenAI / AI Search ecosystems

real client-facing AI solutions or POCs

Key Requirements & Skills

Frequently Asked Questions

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