Computer Vision Engineer

 Responsibilities:

• Develop and optimize computer vision models for tasks like object detection, image segmentation, and multi-object tracking.
• Lead research on novel techniques using deep learning frameworks (TensorFlow, PyTorch, JAX).
• Build efficient computer vision pipelines and optimize models for real-time performance.
• Deploy models using microservices (Docker, Kubernetes) and cloud platforms (AWS, GCP, Azure).
• Lead MLOps practices, including CI/CD pipelines, model versioning, and training optimizations.

• Required Skills:

• Expert in Python, OpenCV, NumPy, and deep learning architectures (e.g., ViTs, YOLO, Mask R-CNN).
• Strong knowledge in computer vision fundamentals, including feature extraction and multi-view geometry with experience in deploying and optimizing models with TensorRT, Open VINO, and cloud/edge solutions.
• Proficient with MLOps tools (MLflow, DVC), CI/CD, and distributed training frameworks.
• Experience in 3D vision, AR/VR, or LiDAR processing is a plus.

Nice to Have:

• Experience with multi-camera vision systems, LiDAR, sensor fusion, and reinforcement learning for vision tasks.
• Exposure to generative AI models (e.g., Stable Diffusion, GANs) and large-scale image processing (Apache Spark, Dask).
• Research publications or patents in computer vision and deep learning.