Data Scientist

Key Responsibilities • Design, develop, and deploy computer vision models for real-world applications such as object detection, image segmentation, OCR, and video analytics • Build scalable, low-latency inference pipelines for real-time or near real-time systems • Develop production-quality Python code with strong engineering principles (modular design, testing, maintainability) • Deploy and manage ML models using APIs and microservices architectures • Work with cloud or edge-based environments for model deployment and optimization • Implement monitoring, model evaluation, and performance tracking (e.g., drift detection, A/B testing) • Collaborate with cross-functional teams including product managers, engineers, and data teams • Analyze data quality, perform dataset annotation/augmentation, and troubleshoot model performance issues • Optimize models for performance, accuracy, and computational efficiency (GPU/latency improvements) Required Skills & Experience • Strong hands-on experience in Computer Vision with real-world, production-deployed systems • Proficiency in Python with solid software engineering fundamentals (OOP, APIs, testing frameworks) • Experience with CV frameworks/tools such as YOLO, OpenCV, Detectron2, PyTorch, TensorFlow • Proven experience deploying ML models into production environments (not just experimentation) • Experience with Docker, Kubernetes, CI/CD pipelines, and model deployment tools (e.g., MLflow) • Strong understanding of APIs and microservices architecture (FastAPI, Flask, etc.) • Experience working with real-time or low-latency systems • Knowledge of model monitoring, drift detection, and performance tuning • Strong understanding of the data lifecycle including annotation, augmentation, and data quality challenges • Ability to debug model performance issues and explain trade-offs in model design