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Tier-1 brand and metro location, but specialist imaging AI seniority limits broad applicant pool.
Imaging AI with clinical safety and regulatory requirements restricts transferability across industries.
Explicit 15+ years and mandatory specialized imaging AI, MLOps and cloud skills make screening highly selective.
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Develop and deploy state-of-the-art computer vision AI models using live camera feeds for clinical platforms in image-guided therapy.
Implement and manage production-grade AI/ML pipelines including multi-class object detection, tracking, and scalable deployment via cloud technologies like AWS, Kubernetes, and Databricks.
Collaborate on data strategies, optimize models for real-time edge and cloud inference, ensure clinical safety and regulatory compliance, and build monitoring dashboards across environments.
15+ years of experience in AI engineering focused on computer vision or imaging AI, including cloud deployment of AI models.
Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, Artificial Intelligence, or related field.
Expertise in CNN architectures (e.g., YOLO, ResNet, EfficientDet), Python programming, and deep learning frameworks such as PyTorch or TensorFlow.
Proficient in MLOps practices, including CI/CD pipelines, model lifecycle management, and tools like MLflow, Airflow, and ClearML.
Senior-level AI engineer with deep specialization in computer vision for healthcare imaging applications and familiarity with clinical regulatory environments.
Hands-on experience in building scalable, robust AI solutions in agile, fast-paced, start-up-like settings with strong ownership mentality.
Proficient in integrating multi-modal data (camera and audio) and observability tools for real-time system monitoring and end-to-end model governance.