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EXL Linkedin · Posted 17d ago

AI Data Engineer

India

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Job Description:


We're looking for an AI Data Engineer to design and build robust data pipelines that power AI/ML and GenAI 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, PySpark, and SQL
  • Develop and maintain scalable data pipelines on Databricks (Delta Lake, notebooks, workflows, cluster optimization)
  • Build data infrastructure to support AI/ML and GenAI 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 PySpark 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 (LangChain, LlamaIndex) 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 NoSQL/vector databases (Pinecone, FAISS, Chroma)

What We Offer

  • Opportunity to work on cutting-edge AI/data infrastructure projects
  • Collaborative, fast-paced environment
  • Competitive compensation and growth path into ML/AI architecture role
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