Computer Vision Engineer

You will be part of Outmarch, an AI-native operational excellence platform for frontline teams. The platform streamlines store operations by providing integrated task management, audits, communications, knowledge-base access, incident tracking, and asset management all in one place. With AI/ML-native image analysis, Outmarch helps retailers identify and resolve in-store compliance issues faster, enhancing customer experience and empowering every role across the organization. As a Computer Vision Engineer at Outmarch, your role will involve developing and deploying advanced vision models to automate planogram compliance, shelf monitoring, and visual merchandising analysis in real store environments. Key Responsibilities: - Design and implement computer vision models for detecting planogram compliance, stock levels, out-of-stock conditions, and visual merchandising KPIs. - Build and manage large-scale image datasets from diverse retail store environments, including annotation, augmentation, and quality control. - Fine-tune and optimize deep learning architectures (e.g., YOLO, Mask R-CNN, EfficientDet) for speed and accuracy on edge devices (TensorFlow Lite, ONNX, Jetson, mobile). - Integrate models into production systems, react native mobile apps, and cloud platforms. - Collaborate with product managers, retailers, and engineers to align AI outputs with retail workflows and operational metrics. - Continuously monitor and improve model performance in live deployments. Qualifications: - Bachelors or Masters degree in Computer Science, Electrical Engineering, or a related field. - 1+ years of experience in computer vision and machine learning applied to real-world image/video data. - Hands-on experience with YOLO or similar object detection frameworks and libraries like OpenCV, TensorFlow, or PyTorch. - Experience deploying models on edge or mobile devices. - Strong understanding of retail store operations or prior work with retail datasets preferred. - Excellent programming skills in Python/C++ and strong problem-solving ability. - Immediate joiners preferred. Preferred Skills: - Knowledge of planogram compliance, shelf monitoring, or retail analytics. - Familiarity with cloud services (AWS, GCP, Azure) and CI/CD for ML pipelines. - Experience optimizing models for low-latency inference in resource-constrained environments. Joining Outmarch will give you the opportunity to shape cutting-edge AI products for the retail industry, work in a fast-growing team tackling high-impact, real-world problems, and be part of a team-oriented environment that values creativity, making a difference, and problem-solving. You will directly contribute to shaping the future of retail solutions and be surrounded by passionate entrepreneurs with experience in solving real-world problems. This is a full-time, permanent position requiring a Bachelor's degree. You should have at least 1 year of experience in computer vision, PyTorch, TensorFlow, OpenCV, object detection frameworks, and AI/ML. The work location is in person at Pune, Maharashtra. You will be part of Outmarch, an AI-native operational excellence platform for frontline teams. The platform streamlines store operations by providing integrated task management, audits, communications, knowledge-base access, incident tracking, and asset management all in one place. With AI/ML-native image analysis, Outmarch helps retailers identify and resolve in-store compliance issues faster, enhancing customer experience and empowering every role across the organization. As a Computer Vision Engineer at Outmarch, your role will involve developing and deploying advanced vision models to automate planogram compliance, shelf monitoring, and visual merchandising analysis in real store environments. Key Responsibilities: - Design and implement computer vision models for detecting planogram compliance, stock levels, out-of-stock conditions, and visual merchandising KPIs. - Build and manage large-scale image datasets from diverse retail store environments, including annotation, augmentation, and quality control. - Fine-tune and optimize deep learning architectures (e.g., YOLO, Mask R-CNN, EfficientDet) for speed and accuracy on edge devices (TensorFlow Lite, ONNX, Jetson, mobile). - Integrate models into production systems, react native mobile apps, and cloud platforms. - Collaborate with product managers, retailers, and engineers to align AI outputs with retail workflows and operational metrics. - Continuously monitor and improve model performance in live deployments. Qualifications: - Bachelors or Masters degree in Computer Science, Electrical Engineering, or a related field. - 1+ years of experience in computer vision and machine learning applied to real-world image/video data. - Hands-on experience with YOLO or similar object detection frameworks and libraries like OpenCV, TensorFlow, or PyTorch. - Experience deploying models on edge or mobile devices. - Strong understanding of retail