Senior Machine Learning Engineer – Computer Vision

Responsibilities:Design, develop, train, evaluate, and deploy production-grade machine learning and deep learning models for computer vision applicationsBuild end-to-end machine learning pipelines covering data ingestion, preprocessing, feature engineering, model training, evaluation, deployment, monitoring, and continuous improvementTrain deep neural networks from scratch on large-scale image datasets and optimize model architectures for accuracy, latency, scalability, and robustnessDevelop computer vision solutions for image classification, object detection, segmentation, localization, image similarity, and feature extractionOwn the complete machine learning lifecycle, including experiment design, hyperparameter optimization, model versioning, model registry, reproducible training pipelines, and model performance monitoringDesign and optimize distributed training pipelines utilizing multiple GPUs and efficiently process large-scale datasetsEvaluate model performance using statistical methods, rigorous experimentation, and business-centric success metricsApply model explainability techniques to validate, interpret, and communicate model predictionsBuild scalable training and inference pipelines using AWS SageMaker and other cloud-native servicesCollaborate closely with Product Managers, Data Scientists, Machine Learning Engineers, Software Engineers, Data Engineers, QA teams, domain experts, and business stakeholders to deliver production-ready AI solutionsDrive continuous model improvements through hypothesis-driven experimentation, error analysis, performance optimization, and data-driven decision makingLead and mentor Machine Learning Engineers, Data Scientists, and Software Engineers.Provide technical direction, establish engineering best practices, conduct architecture and code reviews, and drive execution of large-scale machine learning initiatives