Senior Computer Vision Engineer

About the Role

We are seeking a highly skilled Senior Computer Vision Engineer to join our healthcare AI team and drive the development of advanced medical imaging solutions. This role is ideal for professionals who are deeply passionate about applying Computer Vision and Deep Learning to real-world clinical problems. You will work closely with senior data scientists, software engineers, radiologists, and regulatory stakeholders to design, build, validate, and deploy robust AI models for medical image analysis. The role offers significant exposure to applied research, production-grade AI systems, and clinical-grade validation processes.

Key Responsibilities

Technical Leadership & Model Development

• Lead the end-to-end design, development, training, and optimization of deep learning models for medical imaging tasks, including segmentation, classification, object detection, and localization.
• Architect and experiment with state-of-the-art CNN and transformer-based models (ViT, Swin, hybrid architectures) tailored for healthcare imaging use cases.
• Drive research initiatives focused on improving model accuracy, robustness, explainability, and clinical relevance.

Data Strategy & Model Robustness

• Define and guide data ingestion, preprocessing, augmentation, and annotation strategies, working closely with radiology and clinical experts to ensure medical accuracy and compliance.
• Conduct in-depth error analysis, bias detection, and failure mode investigation across datasets, devices, and patient demographics.
• Design and execute domain generalization and cross-site validation strategies to ensure model performance across diverse imaging devices and institutions.

Engineering & Production Integration

• Bridge the gap between data science and engineering by enabling seamless model integration into production pipelines.
• Optimize models for inference using techniques such as model distillation, pruning, quantization, ONNX conversion, and hardware-aware optimization.
• Package and deploy AI components using Docker-based containerization, ensuring scalability, reproducibility, and Collaboration.
• Collaborate with radiologists, clinical experts, regulatory teams, and product stakeholders to align AI development with clinical workflows, regulatory requirements, and business goals.
• Contribute to documentation and technical reports supporting regulatory submissions, clinical validation, and audits.

Mentorship & Best Practices

• Provide technical leadership and mentorship to junior engineers and data scientists.
• Establish and promote best practices in research methodology, coding standards, experiment tracking, and documentation.
• Participate in architectural discussions and contribute to long-term AI platform strategy.

Required Skills

Core Technical Skills

• Strong programming expertise in Python with hands-on experience using NumPy, pandas, scikit-learn, OpenCV, PIL, and related libraries.
• Solid foundation in machine learning, computer vision, and image processing concepts.
• Extensive hands-on experience with deep learning frameworks such as PyTorch or Keras.
• Proven experience working with CNNs and transformer-based architectures for medical or high-resolution image analysis.

Model Evaluation & Optimization

• Strong experience in model evaluation metrics, cross-validation, statistical analysis, and error diagnostics.
• Practical knowledge of inference optimization and deployment-ready model transformations.

Engineering & Deployment

• Ability to design production-ready AI solutions and integrate them into backend or application workflows.
• Hands-on experience with Docker and containerized AI deployments.

Desired / Good-to-Have Skills

• Familiarity with healthcare and radiology datasets, including DICOM standards and PACS integration.
• Experience with domain adaptation, robustness techniques, and handling dataset shift in medical imaging.
• Exposure to scalable backend systems or AI platforms supporting high-throughput inference.
• Understanding of clinical validation processes and regulatory considerations in healthcare AI.

Qualifications

• Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field.
• Advanced coursework or research experience in Computer Vision or Deep Learning is a strong plus.