Job Description:
We're looking for an AI Data Engineer to design and build robust data pipelines that power AI/ML and Gen AI applications. This role sits at the intersection of traditional data engineering and modern AI infrastructure — you'll be responsible for making data AI-ready, scalable, and production-grade.
Key Roles and Responsibilities
Design, build, and optimize ETL/ELT pipelines using Python, Py Spark, and SQL Develop and maintain scalable data pipelines on Databricks (Delta Lake, notebooks, workflows, cluster optimization) Build data infrastructure to support AI/ML and Gen AI use cases — including feature engineering, embeddings, and vector data pipelines Prepare, clean, and structure data (structured & unstructured) for LLM/RAG-based applications Collaborate with Data Scientists and ML Engineers to operationalize models and AI pipelines Ensure data quality, governance, and performance across pipelines Work with cloud platforms (Azure/AWS/GCP) for data storage, compute, and orchestration Optimize Spark jobs for performance and cost efficiency Required Skills




3–6 years of experience in Data Engineering Strong hands-on expertise in SQL and Python Proficiency in Py Spark for large-scale data processing Working experience with Databricks (Delta Lake, Unity Catalog, notebooks) Exposure to AI/ML data pipelines — vector databases, embeddings, or RAG architecture is a plus Experience with cloud data platforms (Azure Data Factory, AWS Glue, or equivalent) Understanding of data modeling, warehousing, and pipeline orchestration (Airflow/ADF) Familiarity with LLM ecosystems (Lang Chain, Llama Index) is an added advantage Good to Have
Experience in BFSI or analytics-heavy domains Exposure to MLOps tools and CI/CD for data pipelines Knowledge of No SQL/vector databases (Pinecone, FAISS, Chroma) What We Offer
Chance to work on cutting-edge AI/data infrastructure projects Collaborative, fast-paced environment Competitive compensation and growth path into ML/AI arc

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