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Tier-1 brand and Bangalore location raise competition, but senior specialized MLOps focus limits applicant density.
Core MLOps and Azure skills transfer across industries, though healthcare experience gives slight domain preference.
Explicit 8+ years and mandatory Python, ML, and Azure/AKS experience create high shortlisting strictness.
Lead the entire ML system lifecycle including experimentation, model development, production deployment, and ongoing monitoring.
Design and build scalable, cloud-native MLOps pipelines leveraging Azure and Azure Kubernetes Service (AKS).
Mentor teams on MLOps best practices, cloud engineering, and production-grade ML system design, ensuring model performance monitoring and continuous improvement.
8+ years of experience in Machine Learning Engineering, MLOps, Data Science, or related quantitative fields.
Proficiency in Python, Machine Learning, and Deep Learning mandatory.
Experience with cloud environments, specifically Azure and Azure Kubernetes Service (AKS), for ML model deployment and management.
Full-time Bachelor or Master of Engineering degree is required.
Strong background in building scalable and reliable ML pipelines with modern ML methods including Transformers for NLP and representation learning.
Proven ability to independently solve ambiguous problems and deliver end-to-end ML solutions using MLOps best practices like CI/CD and automation.
Experience working cross-functionally with data scientists, engineers, and business stakeholders in a production environment, preferably in healthcare domain or healthcare revenue cycle.