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Tier-1 brand, mid-level generalist role, metro location, and broad ML/MLOps skillset increase competition.
Core data engineering and MLOps skills are broadly transferable across industries.
Explicit 5–6 years plus mandatory Python, PySpark, cloud, MLOps, CI/CD and ML production experience.
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Design, develop, and maintain scalable data pipelines and cloud-based platforms for AI/ML workloads.
Build, deploy, and optimize machine learning models and production-ready AI solutions at enterprise scale.
Implement end-to-end ML lifecycle management including deployment, monitoring, automation, and CI/CD pipelines.
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 data modeling and query optimization.
Hands-on experience with PySpark, cloud platforms (AWS, Azure, or GCP), MLOps, CI/CD, and containerization.
Work Experience Required: 5-6 years in relevant fields. Notice period: Not explicitly mentioned in the JD.
Experienced in operationalizing AI/ML models with strong MLOps and cloud-native architecture skills.
Comfortable collaborating across data scientists, engineers, and business stakeholders to integrate AI/ML into enterprise data platforms.
Skilled in implementing robust, scalable ETL/ELT pipelines and managing model governance and responsible AI practices.