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Tier-1 brand, mid-level popular data/ML role in metro increases qualified applicant density and competition.
Requires specific data engineering and MLOps expertise, moderately transferable across industries.
Explicit 5–6 years plus mandatory Python, PySpark, cloud, CI/CD, and MLOps requirements drive strict filtering.
Design, develop, and maintain scalable data pipelines and cloud data platforms for AI/ML workloads.
Build, deploy, optimize, and support machine learning models and AI solutions at enterprise scale.
Implement CI/CD pipelines and manage end-to-end ML lifecycle including deployment, monitoring, automation, and governance.
5–6 years of experience in Data Engineering, Machine Learning Engineering, or AI-related roles.
Strong programming skills in Python and expert-level SQL including complex data transformations and performance tuning.
Hands-on experience with PySpark, cloud platforms (AWS, Azure, or GCP), and building scalable ETL/ELT pipelines.
Experience with MLOps practices, CI/CD pipelines, containerization, orchestration, and cloud-native architecture.
Proven ability to operationalize AI/ML solutions by collaborating with data scientists, engineers, and business stakeholders.
Experience integrating AI/ML capabilities into enterprise data platforms and business applications.
Strong expertise in distributed data processing and managing scalable data and ML platforms with focus on data/model governance and security.