Computer Vision Engineer

We are seeking a skilled Computer Vision Engineer to develop, optimize, and deploy computer vision and deep learning solutions for real-world applications. The ideal candidate will have experience in image processing, object detection, image segmentation, video analytics, and deep learning. You will collaborate with AI researchers, software engineers, and product teams to build scalable computer vision systems for production environments., * Design, develop, and deploy computer vision models for image and video analysis.

• Build and optimize deep learning models for object detection, image classification, segmentation, tracking, OCR, pose estimation, and anomaly detection.
• Develop data preprocessing, annotation, augmentation, and model training pipelines.
• Fine-tune and optimize CNNs, Vision Transformers (ViTs), and foundation models for domain-specific applications.
• Evaluate model performance using standard computer vision metrics such as mAP, IoU, Precision, Recall, and F1-score.
• Optimize models for inference using ONNX, TensorRT, OpenVINO, or similar optimization frameworks.
• Integrate computer vision models into production applications through APIs and edge devices.
• Collaborate with data scientists, AI researchers, backend developers, and DevOps teams to deploy AI solutions.
• Conduct experiments, benchmark models, and improve accuracy, latency, and scalability.
• Stay current with advancements in computer vision, multimodal AI, and deep learning.

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, Robotics, Electrical Engineering, or a related field.
• 2 5 years of experience in computer vision or deep learning development.
• Strong understanding of image processing, computer vision algorithms, and deep learning fundamentals.
• Proficiency in Python.
• Experience with PyTorch or TensorFlow.
• Hands-on experience with OpenCV and computer vision libraries.
• Familiarity with object detection models such as YOLO, Faster R-CNN, SSD, or RetinaNet.
• Experience with image segmentation models such as U-Net, DeepLab, Mask R-CNN, or Segment Anything (SAM).
• Knowledge of Git, Docker, Linux, and software development best practices., * Experience with Vision Transformers (ViTs), CLIP, DINOv2, Grounding DINO, Florence, or multimodal vision-language models.
• Familiarity with Detectron2, MMDetection, OpenMMLab, or Ultralytics YOLO.
• Experience with OCR, document AI, medical imaging, satellite imagery, robotics, or autonomous systems.
• Knowledge of CUDA, TensorRT, ONNX Runtime, OpenVINO, or GPU optimization.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Familiarity with MLOps tools such as MLflow, Kubeflow, or Docker-based deployment.

Technical Skills

• Python
• PyTorch / TensorFlow
• OpenCV
• NumPy
• Pandas
• Scikit-learn
• Ultralytics YOLO
• Detectron2
• MMDetection
• Hugging Face Transformers
• CUDA
• ONNX
• TensorRT
• Docker
• Git
• Linux
• SQL
• AWS / Azure / Google Cloud

Soft Skills

• Strong analytical and problem-solving skills.
• Excellent debugging and optimization abilities.
• Effective communication and collaboration skills.
• Attention to detail and commitment to delivering high-quality AI solutions.
• Ability to work in Agile and cross-functional environments.

Nice to Have

• Experience with multimodal AI and vision-language models.
• Knowledge of Generative AI and Large Language Models (LLMs).
• Familiarity with edge AI deployment on NVIDIA Jetson, Raspberry Pi, or mobile devices.
• Experience with synthetic data generation and simulation environments.
• Contributions to open-source computer vision projects or published research.

• Competitive salary and performance-based incentives.
• Comprehensive health and wellness benefits.
• Flexible or hybrid work arrangements.
• Learning, certification, and conference sponsorship opportunities.
• Access to modern GPU infrastructure and AI development tools.
• Opportunity to work on cutting-edge computer vision and AI solutions in a collaborative environment.