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Known multinational, popular early-career data-engineer role with broad requirements and likely metro hiring increases competition.
Data engineering skills (Python, SQL, ETL) are highly transferable across industries.
Explicit 0–3 years plus mandatory Python/SQL and PyTorch exposure enforces moderate filtering.
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Design, develop, and maintain scalable data pipelines handling structured and unstructured data for AI/ML applications.
Support data preparation including cleaning, preprocessing, feature engineering, and integration of PyTorch-based models into workflows.
Develop automation scripts, assist in ETL pipeline building, monitor data quality, and collaborate with data scientists and engineering teams.
0–3 years of experience; freshers with relevant training/projects considered.
Bachelor’s degree in Computer Science, Data Science, or related field.
Proficiency in Python programming and SQL data querying.
Basic exposure to PyTorch (or similar ML frameworks), data processing libraries (Pandas, NumPy), and foundational data engineering concepts (ETL, pipelines).
Early-career professional with foundational knowledge and real or academic experience in data pipelines and AI/ML integration.
Comfortable collaborating across teams to implement data-driven solutions involving PyTorch and automation.
Familiar with version control (Git) and able to adapt quickly in dynamic, multi-disciplinary environments.