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Tier-1 brand, mid-level title, metro location, and broad MLOps/data skillset create high competition.
Specialized ML production and cloud data engineering skills are moderately transferable across industries.
Explicit 5–6 years requirement plus extensive mandatory tech, cloud, and MLOps skills imply high strictness.
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Design, develop, and maintain scalable data pipelines and cloud-based platforms supporting AI/ML workloads.
Build, deploy, optimize machine learning models and AI solutions for enterprise-scale applications.
Manage end-to-end ML lifecycle including deployment, monitoring, automation, and implement CI/CD pipelines for data and ML apps.
5–6 years of experience in Data Engineering, Machine Learning Engineering, or AI-related roles.
Proficient in Python, expert-level SQL skills, and hands-on experience with PySpark.
Experience with cloud platforms such as AWS, Azure, or GCP and knowledge of MLOps including model deployment and governance.
Experience implementing CI/CD pipelines and DevOps practices; familiarity with containerization and cloud-native architectures.
Experienced in integrating AI/ML capabilities within enterprise data platforms and business applications.
Skilled at collaborating across data engineering, software engineering, data science, and business teams in Agile environments.
Strong grasp of AI/ML concepts including Generative AI, LLMs, RAG architectures, prompt engineering, and responsible AI practices.