Generative AI Data Engineer

1 week ago

Greater Kolkata Area, India The IT Firm Full time ₹ 15,00,000 - ₹ 25,00,000 per year

About The Role
We are seeking a GenAI Data Engineer to design, build, and optimize data pipelines for unstructured and semi-structured content, integrating advanced AI/ML capabilities. This role combines modern ETL expertise with Vector Database & GenAI integration to support intelligent document processing and semantic search applications.

Key Responsibilities

  • Develop and maintain data ingestion pipelines using Azure Data Factory (ADF) and Databricks for structured and unstructured data.
  • Create notebooks to process PDF and Word documents, including extracting text, tables, charts, graphs, and images.
  • Apply NLP / Embedding Models (e.g., OpenAI, Hugging Face, sentence-transformers) to convert extracted content into embeddings.
  • Store embeddings and metadata into Vector Databases (e.g., FAISS, Pinecone, Milvus, Weaviate, ChromaDB).
  • Design and implement semantic search and retrieval workflows to enable prompt-based query capabilities.
  • Optimize ETL pipelines for scalability, reliability, and performance.
  • Collaborate with data scientists and solution architects to integrate GenAI capabilities into enterprise applications.
  • Follow best practices for code quality, modularity, and documentation.

Required Skills & Experience

  • Proven experience in Azure Data Factory (ADF) and Databricks for building ETL/ELT workflows.
  • Strong programming experience in Python (pandas, PySpark, PyPDF, python-docx, OCR libraries, etc.).
  • Hands-on experience with Vector Databases and semantic search implementation.
  • Understanding of embedding models, LLM-based retrieval, and prompt engineering.
  • Familiarity with handling multi-modal data (text, tables, images, charts).
  • Strong knowledge of data modeling, indexing, and query optimization.
  • Experience with cloud platforms (Azure preferred).
  • Strong problem-solving, debugging, and communication skills.

Nice To Have

  • Experience with knowledge graphs or RAG (Retrieval-Augmented Generation) pipelines.
  • Exposure to MLOps practices and LLM fine-tuning.
  • Familiarity with enterprise-scale document management systems.

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