AI Data Engineer

EXLPune, Maharashtra, Indiafull time
ActiveVerified 2h ago

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

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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