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Tier-1 brand and metro location increase applicant density but senior, specialized imaging-AI requirements moderate competition.
Imaging-AI, clinical safety and regulatory requirements make the role highly industry-specific and less transferable.
Explicit 10+ years and mandatory imaging-AI, MLOps, cloud, and regulatory experience indicate high shortlisting strictness.
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Develop, train, validate, and deploy computer-vision models using CNN-based deep learning architectures (e.g. YOLO, ResNet, EfficientDet) for real-time clinical imaging applications.
Build and manage robust MLOps pipelines for continuous integration, deployment, and monitoring of AI solutions using tools like MLflow, Airflow, and ClearML on cloud platforms such as AWS and Kubernetes.
Collaborate with cross-functional teams to optimize models for performance, ensure regulatory compliance, and create centralized dashboards for real-time system and AI/ML health monitoring.
10+ years of hands-on experience developing and deploying computer-vision or imaging AI models and cloud-based AI engineering.
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, Artificial Intelligence, or related field.
Strong expertise in computer vision techniques including image classification, object detection, and real-time tracking using CNNs.
Experience with cloud platforms (AWS, Databricks, Kubernetes) and MLOps tools for production-grade AI/ML system deployment and lifecycle management.
Experienced in developing multi-class detection AI models specifically for clinical or imaging applications requiring high accuracy and real-time performance.
Skilled in deploying and managing AI at scale with strong knowledge of MLOps and cloud-native technologies to ensure operational stability and scalability.
Able to operate effectively within agile, cross-functional teams supporting advanced development through productization phases, with a focus on regulatory and clinical safety considerations.