AI Data Engineer
Hybrid Pune GurugramNoida Full-time Mid-level Data Engineering
At a glance
Mid-level Data Engineering role at Fa Ewjt Saasfaprod1. Pune · full-time.
Pay not stated Data Engineering salary
RoleData Engineering
SeniorityMid-level
LocationPune
WorkplaceHybrid
EmploymentFull-time
PostedJun 30, 2026
Also hiring inGurugram, Noida
Role brief
dataroles summary, based on the employer's posting.
What you'll do
- Build LLM applications for business tasks with single agents or straightforward multi-agent setups
- Create retrieval-augmented generation flows, from bringing data in through retrieval and answer generation
- Develop Python APIs and services, then connect AI features to enterprise systems, databases and other APIs
- Add response checks and guardrails, and work with data engineering and MLOps teams on quality and deployment
- Record reusable components and practices while improving deployed solutions through monitoring and iteration
What you bring
- You have four to six years of overall experience, including at least one year building GenAI or LLM applications
- You bring production-level Python or PySpark skills, API integration experience and strong work with large datasets
- You have hands-on experience with LLMs, RAG retrieval, agentic AI and an orchestration framework such as LangChain or LangGraph
- You have worked in data engineering, the data science or ML lifecycle, or analytics engineering
- You understand LLM evaluation and limitations, and can connect data engineering platforms such as Fabric, Databricks or Snowflake
Who this fits
This mid-level role suits an engineer combining data foundations with practical LLM and agent development. It is based in Pune, India, with a hybrid work arrangement.
From the employer
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
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