Role Summary: We are looking for a highly skilled Deep Learning Engineer with a strong background in Computer Vision to join our AI team. You will be responsible for researching, designing, training, and deploying deep learning models that allow our systems to process, analyze, and understand visual data. If you have 3+ years of hands-on experience building production-level models for object detection, segmentation, and real-time video analytics, we want you on our team.
Key Responsibilities:
- Model Development: Design, train, and deploy deep learning architectures for core Computer Vision tasks (e.g., Object Detection, Instance/Semantic Segmentation, Image Classification, and Tracking).
- Data Pipeline Management: Preprocess, curate, and augment large-scale image and video datasets to improve model robustness and handle edge cases.
- Optimization & Deployment: Optimize complex neural networks for high-speed, real-time inference in production environments (using tools like TensorRT, ONNX, OpenVINO, or CUDA).
- Cross-Functional Collaboration: Work closely with software engineers, data scientists, and product managers to integrate vision algorithms seamlessly into our broader software architecture.
- Monitoring & Refinement: Monitor deployed models in production, debug inaccuracies, and continuously refine algorithms based on real-world performance metrics (e.g., IoU, F1 Score).
- R&D: Stay up to date with the latest advancements in AI, including Vision Transformers (ViT), YOLO variants, and zero-shot learning models.
Required Qualifications:
- Experience: 3+ years of professional, hands-on experience as a Machine Learning Engineer, Deep Learning Engineer, or Computer Vision Engineer.
- Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Mathematics, Data Science, or a related field.
- Programming Skills: Advanced proficiency in Python.
- AI/CV Frameworks: Deep, practical expertise with deep learning frameworks (PyTorch or TensorFlow) and computer vision libraries (OpenCV).
- Algorithm Expertise: Proven track record working with CNN architectures (ResNet, EfficientNet) and object detection models (YOLO, SSD, Faster R-CNN).
- Math Fundamentals: Strong grasp of linear algebra, calculus, probability, and 3D geometry concepts.
Preferred / Nice-to-Have Skills:
- Experience deploying vision models to Edge computing devices (NVIDIA Jetson, Raspberry Pi, or mobile).
- Familiarity with MLOps practices, MLflow, Docker, and CI/CD for machine learning pipelines.
- Experience with generative models (GANs, Diffusion models) or 3D computer vision (SLAM, Point Clouds).