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Tier-1 brand, mid-level generalist Data Engineer title, metro location, and broad required skills increase competition.
Strong MLOps, PySpark and model-lifecycle requirements make candidates moderately industry-specialized but still transferable across data-driven companies.
Explicit 5–6 years plus mandatory PySpark, MLOps, cloud, SQL, CI/CD and ML lifecycle skills make filters stringent.
Design, develop, and maintain scalable data pipelines and cloud data platforms for AI/ML workloads.
Build, deploy, and optimize machine learning models and ensure end-to-end ML lifecycle management, including deployment, monitoring, and automation.
Implement and maintain CI/CD pipelines and collaborate with cross-functional teams to operationalize AI/ML solutions at enterprise scale.
5–6 years of experience in Data Engineering, Machine Learning Engineering, or AI-related roles.
Strong programming skills in Python and expert-level SQL knowledge including data modeling and query optimization.
Hands-on experience with PySpark and distributed data processing, and expertise in cloud platforms such as AWS, Azure, or GCP.
Experience with MLOps practices, including model deployment, monitoring, and CI/CD pipeline implementation.
Experienced in scalable ETL/ELT pipeline development and cloud-native data platform architecture for AI/ML applications.
Proficient in integrating AI/ML capabilities into enterprise data platforms and enforcing data and model governance.
Skilled in Agile/Scrum environments with ability to collaborate across data scientists, engineers, and business stakeholders for operational AI/ML solutions.