Computer Vision Professional

Educational Requirements

Bachelor of Engineering

Service Line

Topaz COE unit

Responsibilities

• Assist in building and testing CV models for detection, tracking, classification tasks and latest foundation and VLM models.
• Prepare and annotate image/video datasets, support data ingestion and cleaning pipelines
• Contribute to writing and debugging training scripts, model loaders, and preprocessing functions
• Run evaluation jobs and generate performance reports using tools like TensorBoard or custom scripts
• Support error analysis by identifying model weaknesses across edge cases
• Collaborate with senior engineers on integrating models into scalable inference pipelines
• Help visualize model outputs, draw bounding boxes, heatmaps, or segmentation masks for explainability
• Document experiments and code for reproducibility and knowledge sharing
• Used OpenCV, MediaPipe, and scikit-image for preprocessing, motion analysis, and visual overlays
• Integrated DL models with post-processing logic (e.g., NMS, temporal smoothing, event triggering)
• Ensured low-latency inference by profiling and tuning frame-wise preprocessing
• Supported integration of RTSP video feeds and video decoders in test pipelines
• point-cloud ingestion and processing (Open3D/PCL), calibration/registration (ICP/FGR), 3D detection/segmentation with sparse CNNs/PointNet, and RGBLiDARIMU fusion

Additional Responsibilities

• Basic understanding of synthetic data or 3D asset usage in training pipelines
• Familiarity with Git, Linux command line, and Jupyter Notebooks
• Interest in building a career in computer vision and AI for real-world automation
• Bachelors degree in Computer Science, Data Science, or a related technical field
• 13 years of experience or strong internship/projects in computer vision or ML model development

Technical and Professional Requirements

• Good Python skills and working knowledge of PyTorch or TensorFlow
• Familiarity with image processing libraries (e.g., OpenCV, PIL) and dataset tools (e.g., COCO format, YOLO datasets)
• Eagerness to learn, take feedback, and contribute in collaborative development environments
• Exposure to object detection/tracking projects (academic, hackathons, or prior work)

Preferred Skills

• Technology->AI-Data science->Computer Vision
• Technology->AI-Generative AI->Generative AI - Basic
• Technology->AI-Generative AI->Generative AI for Data Analytics