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Tier-1 brand, mid-level title, and broad AI/ML skills create high competition.
Role requires specialized data engineering and MLOps skills, so background transferability is low.
Explicit 5–6 years plus mandatory PySpark, MLOps, cloud, and CI/CD requirements make filters strict.
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Design, develop, and maintain scalable data pipelines and cloud-based data platforms to support AI and ML workloads.
Build, deploy, and optimize machine learning models and AI solutions for enterprise-scale production environments.
Develop and manage end-to-end ML lifecycle processes including deployment, monitoring, automation, and governance with CI/CD pipelines.
5–6 years of experience in Data Engineering, Machine Learning Engineering, or AI-related roles.
Strong proficiency in Python, SQL (including data modeling and query optimization), and PySpark.
Experience with cloud platforms such as AWS, Azure, or GCP and implementing MLOps including CI/CD pipelines.
Work Experience Required: 5–6 years in relevant data engineering and AI/ML roles.
Experienced in integrating AI/ML capabilities into enterprise data platforms and business applications.
Demonstrates deep understanding of AI/ML concepts including Generative AI, LLMs, model lifecycle management, and responsible AI practices.
Proficient in collaborating with cross-functional teams (data scientists, engineers, business stakeholders) in Agile/Scrum environments for operationalizing AI/ML solutions.