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Tier-1 brand, popular ML role, and mid-level skill expectations increase applicant competition.
Core ML engineering skills transfer across industries, but healthcare domain practices raise moderate specialization needs.
Extensive mandatory ML stack, cloud, production deployment, and Responsible AI requirements imply strict screening.
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Lead the design, development, testing, and deployment of scalable AI/ML solutions using Python and modern ML frameworks.
Oversee full ML workflows including data preparation, feature engineering, model development, deployment, and continuous monitoring in production environments.
Collaborate cross-functionally with engineering, product, data science, and UX teams to translate business requirements into robust AI/ML technical architectures and workflows, ensuring Responsible AI principles compliance.
Full-time graduation degree required.
Proven hands-on experience leading AI/ML solution development using Python, SQL, pandas, numpy, and frameworks like TensorFlow, PyTorch, Scikit-Learn.
Experience with cloud ML infrastructure on Azure or GCP; AWS optional.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building and maintaining production-grade ML pipelines with orchestration and experiment tracking tools such as MLflow, Feature Stores, and ONNX.
Strong collaborative skills proven by working closely with data engineering and cross-functional teams to integrate real-time and batch data pipelines and align ML systems with product workflows.
Capable mentor and technical leader who guides best practices in model development, coding standards, cloud-native ML deployment, and ensures scalability, reliability, and operational excellence of ML systems.