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 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

Responsibilities: 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

- Robust 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

Qualifications: 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

📌 AI Data Engineer (Gurugram)
🏢 EXL Service
📍 Gurugram