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Tier-1 brand, mid-level generalist data/ML role in a metro with broad skill requirements increases competition.
Core data engineering and MLOps skills are highly transferable across industries, lowering background sensitivity.
Explicit 5–6 years plus mandatory PySpark, SQL, cloud, and MLOps requirements make filters highly strict.
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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 AI solutions at enterprise scale, managing end-to-end ML lifecycle including deployment, monitoring, and governance.
Implement and maintain CI/CD pipelines and collaborate with cross-functional teams to operationalize AI/ML solutions ensuring data quality and platform performance.
5-6 years of experience in Data Engineering, Machine Learning Engineering, or AI-related roles.
Proficiency in Python, expert SQL skills including data modeling and query optimization, and hands-on experience with PySpark.
Experience with cloud platforms (AWS, Azure, or GCP) and implementing CI/CD pipelines for data and ML applications.
Strong understanding and practical experience in MLOps including model deployment, monitoring, automation, versioning, and governance.
Experienced in building scalable ETL/ELT pipelines, data lakes, and cloud-native data platforms supporting AI/ML.
Proficient in integrating advanced AI/ML concepts such as Generative AI, LLMs, RAG architectures, AI agents, and prompt engineering into enterprise environments.
Comfortable working in Agile/Scrum teams collaborating with data scientists, engineers, and business stakeholders to deliver production-ready AI solutions.