Job Title: Image Processing EngineerJob Summary We are seeking an Image Processing Engineer to design, develop, and optimize advanced image processing and computer vision solutions for real-world applications. The ideal candidate will have expertise in digital image processing, computer vision, machine learning, and deep learning. This role involves developing algorithms for image enhancement, segmentation, object detection, classification, feature extraction, and image analysis while collaborating with AI Engineers, Data Scientists, Software Engineers, and Product teams to deliver high-performance imaging solutions.
Key Responsibilities - Design, develop, and optimize image processing algorithms for analysis, enhancement, restoration, and feature extraction.
- Develop AI-powered computer vision models for image classification, object detection, segmentation, and image recognition.
- Build preprocessing pipelines for image normalization, denoising, resizing, augmentation, and quality improvement.
- Train, fine-tune, and evaluate deep learning models using image datasets.
- Develop solutions for OCR, facial recognition, medical imaging, industrial inspection, satellite imagery, or other domain-specific applications.
- Optimize image processing pipelines for accuracy, speed, and scalability.
- Deploy image analysis models using cloud-native, edge AI, or embedded computing platforms.
- Integrate image processing solutions with enterprise applications, APIs, and real-time systems.
- Monitor production model performance and continuously improve image analysis accuracy.
- Collaborate with cross-functional teams to define requirements and deliver production-ready solutions.
- Document algorithms, experiments, validation results, and deployment procedures.
Required Qualifications - Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Computer Vision, Electrical Engineering, Electronics, or a related field.
- 3+ years of experience in image processing, computer vision, or AI application development.
- Strong programming skills in Python; C++ is a plus.
- Experience with digital image processing techniques and computer vision algorithms.
- Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.
- Experience with OpenCV and scientific computing libraries such as NumPy and SciPy.
- Knowledge of machine learning fundamentals and model evaluation techniques.
- Familiarity with Linux development environments, Git, and Docker.
Preferred Qualifications - Experience with object detection frameworks such as YOLO, Detectron2, MMDetection, or Faster R-CNN.
- Experience with image segmentation models such as U-Net, Mask R-CNN, DeepLab, or Segment Anything Model (SAM).
- Knowledge of image restoration, super-resolution, image generation, or diffusion models.
- Experience with OCR technologies and document AI solutions.
- Familiarity with ONNX Runtime, TensorRT, OpenVINO, or edge AI deployment.
- Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
- Knowledge of MLOps, model serving, and AI deployment practices.
Technical Skills - Python
- C++ (preferred)
- OpenCV
- NumPy
- SciPy
- Scikit-learn
- PyTorch
- TensorFlow
- YOLO
- Detectron2
- MMDetection
- Faster R-CNN
- U-Net
- Mask R-CNN
- DeepLab
- Segment Anything Model (SAM)
- ONNX Runtime
- TensorRT
- OpenVINO
- Docker
- Git
- Linux
- REST APIs
- AWS / Azure / Google Cloud Platform
Soft Skills - Strong analytical and problem-solving skills
- Excellent communication and teamwork
- Attention to detail and accuracy
- Ability to work in cross-functional Agile teams
- Innovation and continuous learning mindset
- Strong debugging and troubleshooting abilities
Nice to Have - Experience with medical imaging (DICOM), remote sensing, hyperspectral imaging, or industrial vision systems
- Knowledge of image annotation tools and dataset management
- Experience with edge AI devices such as NVIDIA Jetson or Client-based vision platforms
- Familiarity with Generative AI for image enhancement or synthetic data generation
- Contributions to open-source computer vision projects or AI research
- Computer vision, cloud, or AI-related certifications
Key Performance Indicators (KPIs) - Image classification, detection, and segmentation accuracy
- Image processing pipeline performance and latency
- Model precision, recall, F1-score, and mAP
- Production system reliability and uptime
- Reduction in image processing errors and false detections
- Successful deployment of AI-powered imaging solutions
- Improvement in processing efficiency and resource utilization
- Timely delivery of computer vision features and enhancements
Location Hybrid / Remote / On-site (as applicable)
Employment Type Full-time
