Generative AI Data Engineer

2 days ago

Multiple Locations, India NS Global Corporation 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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