Computer Vision Engineer

Requirements :

- 5+ years of hands-on experience in a computer vision or deep learning role.

- Experience with industrial/safety inspection datasets (e. g., PPE detection, visual defect classification).

- Familiarity with MLOps tools like MLflow, DVC, or ClearML.

- Experience with model optimization and deployment frameworks (ONNX, TensorRT, OpenVINO).

- Exposure to real-time or edge inference performance constraints.

- Contributions to open-source, research publications, or competitive CV challenges (e. g., Kaggle).

Technical Qualification :

- Good understanding of CNNs, transfer learning, data augmentation, and overfitting mitigation.

- Familiarity with basic software engineering practices (git, code reviews, unit testing).

- Mathematical Background : Solid grasp of linear algebra, probability, and optimization as applied in ML.

- Languages/Frameworks : Python, PyTorch, Tensorflow, TorchVision, FastAPI, OpenCV.

- Model Tools : ONNX, TensorRT, Albumentations. DevOps : Docker, Git, Azure/AWS.

- Infra : Jetson devices, cloud APIs, SQL/NoSQL databases.