Company Description
Vision Logix is an innovative software development company specializing in Computer Vision (CV) solutions. We empower businesses across various industries to unlock the vast intelligence hidden within their visual dataimages, videos, and real-time streamsand transform it into actionable insights and automated processes.
Role Description
We are looking for a skilled and passionateComputer Vision Engineerwith2+ years of professional experienceto join our team. You will be responsible for designing, developing, and deploying robust computer vision and deep learning models for our products. This role is ideal for an engineer who is ready to take ownership of projects and contribute directly to production-ready systems.
Qualifications
- Experience:2+ years of professional experienceworking as a Computer Vision Engineer or Machine Learning Engineer with a focus on computer vision.
- Education:Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related quantitative field, or equivalent practical experience.
- Programming Expertise:Strong proficiency inPythonand extensive experience with core computer vision libraries, primarilyOpenCV.
- Deep Learning Frameworks:Hands-on experience with at least one major deep learning framework:PyTorch(preferred) orTensorFlow/Keras.
- Model Development:Proven experience developing, training, fine-tuning, and evaluating state-of-the-art computer vision models for real-world applications.
- Maturity:Experience taking a model from the research/prototype phase through to a production environment.
Bonus Considerations
- Experience withMLOpsprinciples and tools for automated model deployment, scaling, and monitoring.
- Knowledge ofcontainerization technologiessuch asDockerand orchestration tools likeKubernetes.
- Experience optimizing models for specific platforms, such asTensorRT,ONNX, or mobile/edge frameworks.
- Experience utilizing cloud computing platforms (e.g., AWS, Azure, GCP) for model training and deployment.
- Strong understanding of performance metrics beyond simple accuracy, such as mAP, IoU, recall/precision trade-offs, and inference time.
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