Deep Learning Engineer - Computer Vision

We are looking for a skilled Deep Learning Engineer with strong expertise in object detection and segmentation model development. The ideal candidate should have hands-on experience with deep learning frameworks, data preparation, and model deployment on various environments, including Docker and Linux/Windows systems. This role involves both applied implementation and research-driven development of computer vision models for real-world applications.

Key Responsibilities:

Object Detection & Segmentation:

• Hands-on experience in implementing and fine-tuning object detection models such as YOLOvX, EfficientDet, Faster R-CNN, SSD, and Mask R-CNN.
• Work on real-time object detection use cases with performance optimization.

Programming & Frameworks:

• Strong programming experience in Python.
• Proficiency in deep learning frameworks such as TensorFlow, PyTorch, OpenCV, and ONNX.

Data Preparation & Annotation:

• Expertise in dataset creation, annotation, augmentation, and preprocessing.
• Experience with tools like Roboflow, CVAT, LabelImg, and similar annotation platforms.

Model Research & Development:

• Ability to understand and implement state-of-the-art research papers.
• Participate in R&D activities to improve model accuracy, efficiency, and robustness.

System & Deployment Experience:

• Proficient with Linux and Windows operating systems for model development and deployment.
• Experience with Docker for containerizing ML models and setting up reproducible environments.
• Familiarity with model deployment practices for local, cloud, or edge systems.

Collaboration & Documentation:

• Work closely with cross-functional teams including data engineers, software developers, and product managers.
• Document code, experiments, results, and deployment procedures.

Preferred Experience & Skills:

• 2+ years of hands-on experience in deep learning, particularly in computer vision and object detection.
• Exposure to model optimization techniques (e.g., quantization, pruning).
• Knowledge of deployment pipelines using Docker, and optionally Kubernetes.
• Experience with edge deployment on devices like NVIDIA Jetson or using frameworks like TensorRT/OpenVINO.
• Strong problem-solving skills and the ability to work independently and collaboratively in a team environment.
• Familiarity with experiment tracking tools (e.g., MLflow, Weights & Biases) is a plus.