Position: AI Data Engineer

Level: Senior

Experience: 8–10 years

Location: Noida (hybrid)

Budget: around 15-20 LPA

About the role

You own the data layer that makes our AI products actually work. Every RAG system we ship lives or dies on what you build — the ingestion pipelines that pull from messy enterprise sources, the embedding and chunking choices that decide whether retrieval is precise or noisy, the vector infrastructure that has to stay fast and fresh in production, and the OCR/NLP pre-processing that handles real-world Indian enterprise data (Hinglish, regional scripts, scanned PDFs, semi-structured forms).

This is not a traditional data warehousing role. You are building AI-native data infrastructure — vector databases, embedding pipelines, retrieval evaluation harnesses, document ingestion at scale — for clients in BFSI, Healthcare, Manufacturing, and Retail/D2C.

Required qualifications
- 8–10 years in data engineering, with the last 2+ years building data infrastructure for LLM/RAG applications in production (not just analytics or BI pipelines).




- Strong Python (pandas, PySpark, FastAPI for data services) and robust SQL.
- Production experience with at least two vector databases — Pinecone, Weaviate, pgvector, Qdrant, Milvus, or Chroma.
- Hands-on experience with embedding models — OpenAI embeddings, Cohere, BGE family, sentence-transformers, or equivalent — including knowing when each is the right choice.
- Production experience with at least one orchestration framework — Airflow, Dagster, Prefect, or equivalent.
- Hands-on OCR/document AI experience — AWS Textract, Azure Document Intelligence, Google Document AI, Tesseract, PaddleOCR, or Unstructured.io.
- Cloud experience (AWS, Azure, or GCP) covering storage, compute, and managed data services.
- Demonstrable understanding of retrieval evaluation — you can explain why a RAG system is failing using metrics, not anecdotes.

Preferred qualifications
- Experience with multilingual NLP — Indic langu

📌 AI Data Engineer (India)
🏢 Sparix Global
📍 India