• Research, develop, and validate computer vision algorithms (object detection, segmentation, anomaly detection, tracking, few shot approaches) for automated and generalised defect and anomaly detection. • Train and optimize deep learning models (e.g., CNNs, YOLO, Vision Transformers) for high-accuracy and high-speed computer vision-based data analysis. • Develop models to identify defects based on variations from normal images. • Design and evaluate vision‑language models to interpret images and support anomaly‑detection tasks. • Stay up to date with the latest advancements in AI, Machine Learning, and Computer Vision literature to maintain competitive advantage.

Role Description We are seeking a Computer Science Subject Matter Expert to contribute to the development of next-generation AI systems by applying deep technical expertise to real-world computer science challenges. In this role, you will analyze, evaluate, and create high-quality technical content that enhances the reasoning, accuracy, and performance of AI models. Prior experience in Artificial Intelligence is not required—your expertise in Computer Science is the primary qualification. Key Responsibilities - Technical Analysis & Problem Solving - Analyze and evaluate complex computer science problems, solutions, and methodologies across various domains. - Assess technical approaches and recommend well-structured, scalable, and efficient solutions based on industry best practices. - Ensure technical accuracy, logical consistency, and clarity in all deliverables. - Content Development & Documentation - Author, review, and refine technical documentation, including system design documents, research papers, technical specifications, and architecture write-ups. - Develop real-world computer science scenarios, datasets, and case studies to support AI model training and evaluation. - Create structured technical explanations that improve AI understanding and reasoning. - AI Model Evaluation - Review AI-generated responses for technical correctness, completeness, and adherence to computer science principles. - Identify inaccuracies, inconsistencies, and opportunities for improvement while providing constructive feedback. - Support the evaluation and continuous enhancement of AI-driven tools through domain expertise. - Collaboration & Quality Improvement - Work closely with project coordinators and subject matter experts to refine task guidelines and improve data quality. - Provide clear written and verbal feedback to enhance AI model relevance, robustness, and performance. - Collaborate in a remote environment while maintaining high-quality deliverables and documentation. Qualifications - Bachelor's, Master's, or PhD in Computer Science or a related field. - Strong understanding of core computer science concepts, including algorithms, data structures, operating systems, databases, networking, and software engineering. - Experience writing technical documentation, research papers, system design documents, or technical reports. - Excellent analytical, problem-solving, and critical thinking skills. - Strong written and verbal communication skills with the ability to explain complex technical concepts clearly. - Ability to work independently and deliver high-quality work in a remote environment. Preferred Qualifications - Expertise in one or more specialized domains such as: - Algorithms & Data Structures - Distributed Systems - Computer Networks - Databases - Artificial Intelligence & Machine Learning - Cloud Computing - Cybersecurity - Experience participating in technical reviews, research projects, or open-source contributions. - Proven ability to simplify complex technical concepts for both technical and non-technical audiences. - Familiarity with current trends and emerging technologies in computer science. Must-Have Skills - Computer Science - Algorithms & Data Structures - System Design - Technical Documentation - Problem Solving Good-to-Have Skills - Distributed Systems - Databases - Networking - Artificial Intelligence - Research & Publications - Open Source Contributions - Software Engineering - Technical Reviews

Role Description We are looking for a Senior Computer Vision Engineer to own applied model development across computer vision, machine learning, and data science use cases — building and deploying solutions for object detection, image segmentation, classification, and video analysis. You will evaluate and fine-tune model architectures using PyTorch and TensorFlow, build broader ML models for forecasting and anomaly detection, and apply practical knowledge of computer vision hardware including cameras, sensors, and edge devices. Qualifications - 3 to 5 years of professional experience in Computer Vision, Machine Learning, Data Science, or a related field; - Degree in Computer Science, Engineering, Data Science, Mathematics, or a related discipline (or equivalent practical experience); - Strong, production-level proficiency in Python; - Deep hands-on experience with leading deep learning frameworks, specifically PyTorch and/or TensorFlow; - Proven track record of building and deploying models for detection, segmentation, classification, and image/video analysis; - Solid understanding of broader ML and data science techniques (time-series modeling, forecasting, anomaly detection, regression, and clustering); - Upper-intermediate English level. Requirements - Practical experience working with computer vision hardware, including cameras, sensors, and lighting setups; - Familiarity with deploying models on edge devices; - Strong understanding of how physical and real-world constraints impact data quality, model training, and inference. Benefits - Professional growth: Mentorship, TechTalks, and personalized growth roadmaps. - Competitive compensation: USD-based pay with education, fitness, and team activity budgets. - Exciting projects: Modern solutions with Fortune 500 and top product companies. - Flextime: Flexible schedule with remote and office options.

• Develop and improve computer vision models for sports video, including player and ball detection, tracking, event recognition, and identity association. • Build and improve camera calibration, homography, and field-registration solutions that map image coordinates into normalized field coordinates. • Analyze existing computer vision pipelines, establish baselines, identify weak links, and recommend practical improvements. • Improve tracking robustness across different stadiums, camera placements, broadcast styles, video qualities, and environmental conditions. • Design experiments covering data acquisition, dataset creation, augmentation, model training, fine-tuning, evaluation, and deployment readiness. • Analyze failure modes and implement improvements that increase accuracy, reliability, scalability, and robustness. • Adapt existing models and pipelines to support new sports, leagues, camera configurations, and video sources. • Partner with data teams on labeling workflows, dataset quality, validation processes, and human-in-the-loop improvement cycles. • Work closely with software, platform, and DevOps engineers to deploy computer vision models and pipelines into production environments. • Improve inference performance, scalability, monitoring, and operational reliability. • Establish evaluation metrics, testing processes, and quality controls to ensure model performance remains consistent over time. • Lead initiatives end-to-end, from early technical discovery and prototyping through production deployment and ongoing improvement. • Contribute to system design decisions that integrate computer vision, machine learning, backend services, operations, and client workflows. • Communicate technical tradeoffs clearly with internal teams, client stakeholders, and engineering leadership.