Artificial Intelligence Data Engineer

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