Senior Computer Vision Engineer (Python / OpenCV)

# Senior Computer Vision Engineer (Python / OpenCV) We are looking for an experienced Computer Vision engineer with deep expertise in **Python** and **OpenCV** to optimize an existing production image-processing pipeline. This is a highly technical project focused on building a **fast, accurate, and production-ready visual detection system**. The successful candidate should have extensive experience with classical computer vision techniques and performance optimization. This is **not** an AI/LLM project. We are specifically looking for someone with strong OpenCV experience who understands how to build robust, high-performance detection systems. ## Required Skills * Expert Python * Expert OpenCV * Template Matching * Multi-scale Template Matching * Image Preprocessing * Feature Detection & Image Alignment * Performance Optimization * Confidence Scoring * Bounding Box Detection * Production Computer Vision Systems * High-Throughput Image Processing ## Project Objectives You will develop and integrate a detection module capable of identifying predefined visual patterns within images. The solution must: * Detect target visual elements with extremely high accuracy * Return bounding box coordinates * Return a confidence score for every detection * Produce **near-zero false positives** * Produce **near-zero false negatives** * Operate consistently across varying image resolutions and scaling factors * Be optimized for high FPS / high-throughput processing * Be suitable for production deployment Speed is just as important as accuracy. ## Performance Requirements The implementation should demonstrate: * High images processed per second (FPS) * Low per-image latency * Efficient OpenCV implementation * Minimal computational overhead * Stable performance across large datasets * Scalability for continuous processing workloads We are looking for a solution that maintains production-level accuracy while processing images at high speed. ## Validation Requirements A standalone Python validation script is required. The script should: * Run detection against an entire directory of images * Execute extensive automated testing * Support both positive and negative test cases * Generate a validation report The report should include: * Detection result * Bounding box coordinates * Confidence score * Processing time per image * FPS / Images processed per second * Accuracy * Precision * Recall * False Positive Rate * False Negative Rate * Summary statistics across the complete dataset The testing framework should make it easy to benchmark improvements and verify reliability over large batches of images. ## Deliverables * Production-ready OpenCV implementation * Clean, maintainable Python code * Integration into an existing Python pipeline * Standalone validation/testing script * Performance benchmark report * Clear documentation explaining the implementation and tuning methodology ## What We're Looking For We are looking for someone who has built real-world Computer Vision systems—not just prototypes. Experience with any of the following is highly desirable: * High-speed image processing * Industrial computer vision * OCR replacement or optimization * Screenshot or video-frame analysis * Large-scale visual detection systems * Performance tuning and benchmarking ## To Apply Please include: 1. Examples of similar Computer Vision or OpenCV projects. 2. Explain your approach to achieving near-zero false positives and false negatives. 3. Describe how you would validate a production detection system before deployment. 4. Explain how you optimize OpenCV pipelines for high FPS and low latency. 5. Include any benchmark results from previous projects (FPS, images/sec, latency, accuracy, etc.). 6. Briefly explain your preferred template matching methodology and why you would choose it over alternative approaches. Applications without relevant OpenCV experience or examples of similar work will not be considered.