Key Responsibilities

• Design, build, and optimize scalable data pipelines using Python, Pandas, and cloud services for enterprise analytics and AI deployment
• Develop and maintain data workflows for large datasets, supporting data cleansing, feature engineering, and model inference processes
• Collaborate with data scientists, AI architects, and platform teams to facilitate seamless data and model integration
• Implement automation for data ingestion, processing, and system deployment using CI/CD pipelines and containerization
• Troubleshoot and resolve performance bottlenecks, optimize data storage/processing, and enforce data security standards
• Create technical documentation and design standards supporting data and AI workflows
• Lead initiatives on data governance, quality, and compliance across data pipelines and models
• Support innovation by evaluating new tools, cloud features, and AI techniques for organizational benefit

Requirements

• Minimum of 6 years of practical experience in data engineering, AI workflows, or large-scale data pipelines
• Extensive hands-on experience with Python, including multi-threading and OOP design patterns
• Practical knowledge of Pandas and data storage formats such as Parquet and Delta Lake
• Experience developing data pipelines on cloud platforms (Azure, AWS, GCP) supporting AI/ML workflows
• Experience with databases such as Oracle and ORM frameworks (SQLAlchemy, Django ORM)
• Proficiency with Docker and Kubernetes for deployment
• Strong understanding of CI/CD practices, automated testing (pytest), and Git/GitHub workflows
• Knowledge of AI engineering tools such as GitHub Copilot or similar code assistants
• Bachelor's or Master's degree in Data Science, Computer Science, or related field
• Cloud certifications (AWS, Azure, GCP) or ML framework certifications are advantageous

Skills Required