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AI Data Engineer

AI Data Engineer

Infosys
Early Applicant
  • Posted 10 hours ago
  • Be among the first 10 applicants

Job Description

Good to have skills: SQL, Python, Apache Spark, Airflow, Data Modeling, data engineering, ai

Key Responsibilities:

  • Design, build, and maintain robust ETL/ELT pipelines to ingest, transform, and curate data from multiple sources.
  • Develop and optimize data models and curated datasets to support analytics, reporting, and AI/ML workloads.
  • Implement data quality checks, validation rules, and monitoring to ensure accuracy, completeness, and reliability.
  • Enable GenAI initiatives by preparing high-quality datasets for downstream consumption (e.g., feature-ready and retrieval-ready data).
  • Collaborate with cross-functional teams to gather requirements, define data contracts, and deliver reusable data assets.
  • Troubleshoot pipeline failures and performance bottlenecks; improve scalability, latency, and cost efficiency.
  • Maintain documentation for pipelines, transformations, lineage, and operational runbooks to support maintainability. Minimum Qualifications:
  • BTECH, MTECH, MCA, MSC or equivalent education.
  • 3–5 years of experience in data engineering with hands-on ownership of production-grade pipelines.
  • Strong experience in ETL processes including extraction, transformation, orchestration, and scheduling.
  • Working exposure to GenAI-oriented data preparation needs and supporting AI/ML data workflows.
  • Ability to collaborate with stakeholders to translate requirements into scalable data solutions. Preferred Qualifications:
  • Experience designing scalable data architectures and implementing reusable data frameworks for multiple use cases.
  • Familiarity with building datasets for GenAI use cases such as retrieval workflows and knowledge augmentation patterns.
  • Proven ability to improve pipeline reliability through automation, alerting, and proactive monitoring.
  • Strong problem-solving skills with a track record of optimizing transformations and reducing end-to-end processing time.
  • Experience working in agile teams and contributing to code reviews, documentation, and engineering best practices.

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