Senior Computer Vision Engineer

We're Hiring: Senior Computer Vision EngineerLocation: United Arab Emirates (Remote)Employment Type: Full-TimeExperience Level: SeniorWork Arrangement: Fully RemoteAbout UsWe are a globally focused organization committed to developing intelligent technologies, digital products, and data-driven solutions that improve operational efficiency, customer experiences, and business performance across diverse markets.Our multidisciplinary teams collaborate across Artificial Intelligence, Machine Learning, Software Engineering, Data Science, Product, Robotics, Automation, Operations, and Research to develop scalable computer vision systems that convert visual data into reliable and actionable intelligence.The RoleWe are seeking an experienced Senior Computer Vision Engineer to lead the design, development, optimization, deployment, and continuous improvement of advanced computer vision and image-processing solutions.The ideal candidate will combine strong expertise in computer vision, deep learning, machine learning, image processing, and software engineering to develop production-ready systems for object detection, image classification, segmentation, tracking, recognition, video analytics, visual inspection, and other computer vision applications.Key ResponsibilitiesDesign and develop advanced computer vision systems for real-world applications.Lead computer vision projects from problem definition and data preparation through model development, validation, deployment, and optimization.Translate business and operational requirements into computer vision solutions and technical specifications.Analyze image, video, sensor, and multimodal datasets to identify suitable computer vision approaches.Design and implement image-processing and computer vision pipelines.Develop object detection, image classification, semantic segmentation, instance segmentation, object tracking, pose estimation, and recognition models.Develop video analytics solutions for real-time and offline applications.Apply deep learning architectures including CNNs, transformers, vision transformers, and other appropriate approaches.Evaluate and select suitable computer vision models, algorithms, architectures, and frameworks.Develop and optimize models using frameworks such as PyTorch, TensorFlow, OpenCV, or equivalent technologies.Perform image preprocessing, augmentation, normalization, enhancement, filtering, feature extraction, and transformation.Develop robust techniques for handling challenging lighting, weather, perspective, occlusion, motion blur, image noise, and other real-world conditions.Design data pipelines for collecting, cleaning, labeling, validating, and managing visual datasets.Establish image and video annotation standards, quality-control procedures, and dataset-management processes.Work with data-labeling teams and external annotation providers to improve training-data quality.Analyze dataset coverage, class imbalance, bias, edge cases, and data-distribution differences.Develop strategies for dataset expansion, augmentation, synthetic data generation, and hard-example mining.Train, fine-tune, and evaluate computer vision and deep learning models.Conduct hyperparameter optimization and systematic model experimentation.Establish appropriate evaluation metrics for different computer vision use cases.Analyze precision, recall, F1 score, IoU, mAP, ROC-AUC, latency, throughput, and other relevant performance indicators.Perform model error analysis and identify recurring failure patterns.Develop techniques to improve model robustness, accuracy, generalization, and reliability.Investigate false positives, false negatives, missed detections, misclassifications, and other model errors.Conduct benchmarking against alternative models, architectures, datasets, and implementation approaches.Optimize computer vision models for inference speed, memory consumption, compute efficiency, and scalability.Deploy computer vision models into production environments, cloud platforms, edge devices, embedded systems, or specialized hardware.Develop real-time inference pipelines for image and video applications.Optimize models using techniques such as quantization, pruning, distillation, batching, and hardware acceleration where appropriate.Work with GPUs, CPUs, edge accelerators, embedded systems, and other computing platforms.Integrate computer vision models with APIs, applications, robotics systems, automation platforms, databases, and enterprise software.Collaborate with Software Engineering teams to develop reliable production-grade computer vision services.Develop scalable APIs, inference services, data pipelines, and supporting software components.Establish model versioning, deployment, monitoring, rollback, and lifecycle-management processes.Implement computer vision monitoring to detect model degradation, data drift, performance changes, and unexpected behavior.Develop automated testing and validation processes for computer vision systems.Establish reproducible machine-learning and computer vision development workflows.Maintain technical documentation covering models, datasets, architectures, experiments, assumptions, limitations, and deployment configurations.Work with Product and Operations teams to define acceptance criteria and measurable outcomes for computer vision solutions.Conduct proof-of-concept development, technical feasibility assessments, and prototype validation.Support computer vision pilots and transition successful prototypes into production systems.Collaborate with Robotics and Automation teams on perception systems, object recognition, localization, and scene understanding.Support applications involving autonomous systems, industrial inspection, warehouse automation, security, retail analytics, mobility, or intelligent infrastructure where applicable.Evaluate emerging computer vision technologies, research developments, foundation models, multimodal models, and vision-language models.Assess the practical application of generative AI, vision-language models, multimodal AI, and synthetic-data technologies.Monitor academic research, open-source developments, industry benchmarks, and emerging computer vision techniques.Conduct technical evaluations of third-party computer vision platforms, models, APIs, hardware, and technology providers.Manage relationships with external technology vendors, research partners, consultants, and specialist providers where required.Contribute to intellectual property development, technical research, publications, patents, or internal innovation initiatives where applicable.Promote responsible AI practices covering privacy, fairness, transparency, security, and appropriate use of visual data.Ensure computer vision solutions comply with applicable data-protection, cybersecurity, regulatory, and organizational requirements.Mentor junior computer vision engineers, machine learning engineers, and data scientists.Conduct technical reviews and provide guidance on architecture, model development, experimentation, and production implementation.Establish computer vision engineering standards, development practices, and reusable frameworks.Provide leadership with regular updates on computer vision projects, model performance, technical risks, development progress, and innovation opportunities.Key Performance IndicatorsModel accuracyObject detection mAPPrecision and recallF1 scoreIntersection over UnionClassification accuracySegmentation performanceTracking accuracyFalse-positive rateFalse-negative rateModel inference latencyReal-time processing performanceFrames processed per secondGPU and compute utilizationMemory efficiencyModel deployment success rateProduction model availabilityModel degradation rateData-drift detectionModel monitoring coverageDataset qualityAnnotation accuracyDataset coverageTraining-data completenessExperimentation cycle timeModel development cycle timePrototype-to-production conversionComputer vision project deliveryProduction incident rateModel rollback frequencySystem reliabilityAPI and inference-service performanceComputer vision pipeline efficiencyCloud and infrastructure cost efficiencyEdge-device performanceTechnical debt reductionAutomated testing coverageDocumentation completenessStakeholder satisfactionResearch and innovation contributionModel improvement from releasesTechnology evaluation completionSecurity and privacy complianceTeam development and mentoringIdeal CandidateThe successful candidate should have strong experience in computer vision, deep learning, machine learning, image processing, artificial intelligence, robotics perception, video analytics, or visual intelligence, preferably within technology, robotics, manufacturing, automotive, logistics, retail, security, healthcare, or other AI-intensive environments.The candidate should demonstrate:Strong understanding of computer vision fundamentals and modern deep-learning techniques.Proven experience developing and deploying production-grade computer vision systems.Strong knowledge of image processing, feature extraction, object detection, segmentation, classification, and tracking.Experience with deep learning architectures such as CNNs, transformers, Vision Transformers, or related models.Strong programming skills in Python and experience with modern machine-learning frameworks.Practical experience with PyTorch, TensorFlow, OpenCV, or equivalent computer vision technologies.Strong understanding of model training, evaluation, optimization, and deployment.Experience working with large-scale image and video datasets.Strong understanding of data annotation, dataset management, augmentation, and quality control.Experience performing model error analysis and improving performance on difficult edge cases.Knowledge of model optimization, quantization, pruning, distillation, or hardware acceleration is highly desirable.