Roles & Responsibilities

- Lead the R&D; team's efforts in product development, guiding juniors, and owning the product.

- Design and develop Computer Vision solutions for automated quality inspection and defect detection.

- Develop and train Deep Learning models for:

- Object Detection

- Image Classification

- Semantic / Instance Segmentation

- Visual Anomaly and Defect Detection

- Apply classical image processing techniques using OpenCV or equivalent libraries for preprocessing, feature extraction, measurement, filtering, and post-processing.

- Build datasets, define annotation requirements, perform data augmentation, and evaluate model performance.

- Perform model validation and error analysis focusing on false positives, false negatives, precision, recall, and defect-level performance.

- Develop complete QC pipelines combining model predictions with appropriate business/inspection logic.

- Deploy and optimize Computer Vision models for real-time production environments.

- Troubleshoot real-world QC challenges such as variations in lighting, reflections, camera angles, product positioning, backgrounds, and defect appearance.

- Work with production and quality teams to translate inspection requirements into practical Computer Vision solutions.

- Continuously improve deployed models based on production data and newly identified defect scenarios.

Camera & Vision System Experience

- Experience working with industrial cameras, IP cameras, and video streams.

- Understanding of camera selection and compatibility based on resolution, frame rate, field of view, working distance, and inspection requirements.

- Familiarity with camera calibration, perspective correction, ROI configuration, and image acquisition.





- Understanding of the impact of lighting, lens selection, exposure, motion blur, reflections, and camera positioning on inspection accuracy.

- Experience integrating and validating cameras as part of an end-to-end QC system.

Preferred Experience

- 4+ years of experience.

- Prior experience building and deploying Computer Vision-based QC / automated inspection systems in production.

- Experience detecting manufacturing defects, surface defects, dimensional abnormalities, or product-quality issues.

- Experience with multi-camera vision systems.

- Experience with TensorRT, ONNX, NVIDIA GPUs, Jetson, or similar edge deployment platforms.

- Familiarity with industrial automation, PLC integration, or manufacturing-line systems.

- Experience optimizing Computer Vision pipelines for real-time inference.

Required Skills

- Strong programming experience in Python.

- Solid understanding of Computer Vision and Deep Learning fundamentals.

- Hands-on experience with PyTorch / TensorFlow, OpenCV, or equivalent frameworks.

- Experience with modern object detection and segmentation architectures.

- Understanding the model training, validation, performance metrics, and inference pipelines.

- Experience deploying trained models into production applications.

- Familiarity with GPU-based inference and model optimization.

Good to Have

- Experience with Vision Transformers or newer Computer Vision architectures.

- Exposure to Vision-Language Models (VLMs) for industrial or visual inspection applications.

- Experience with synthetic data generation or handling limited/imbalanced defect datasets.

- Basic understanding of APIs, databases, Docker, and Linux-based deployment environments.

📌 Senior Computer Vision Engineer (Chennai)
🏢 Spritle Software
📍 Chennai