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Tier-1 brand plus metro location increase applicant density despite niche senior MLOps requirement.
Core MLOps and cloud skills transfer across industries, though healthcare domain preference adds moderate specificity.
Explicit 8+ years and mandatory MLOps/Azure/ML technical skills create strict screening.
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Own end-to-end ML system lifecycle including experimentation, model development, production deployment, and monitoring.
Design and build scalable, cloud-native MLOps pipelines on Azure with focus on Kubernetes (AKS) and implement best practices like CI/CD, versioning, and reproducibility.
Collaborate cross-functionally and mentor teams on MLOps practices while ensuring continuous model improvement in production environments.
Minimum 8+ years of experience in Machine Learning Engineering, MLOps, Data Science, or related quantitative fields.
Strong proficiency in Python, machine learning, and deep learning with hands-on experience deploying and managing ML models on Azure and AKS.
Bachelor or Master of Engineering degree required.
Work Experience Required: 8+ years
Experienced in taking ML models from experimentation to production using MLOps best practices (CI/CD, automation, orchestration).
Skilled in building scalable, reliable ML pipelines for large-scale, high-dimensional datasets with expertise in modern ML methods including Transformers for NLP.
Able to independently manage ambiguous problems and collaborate effectively with cross-functional teams; healthcare domain experience is a plus.