Jobs → Engineering → ML Ops Engineer

About the Role We are seeking an experienced ML Ops Engineer to develop, deploy, and maintain machine learning and artificial intelligence solutions supporting analytical and automation initiatives. The ideal candidate will have hands-on experience across the entire model lifecycle. Key Responsibilities • Develop, train, test, and implement ML/AI models (NLP, computer vision, predictive analytics) and deploy them into production environments • Build and maintain automated MLOps pipelines to streamline model lifecycle management, continuous integration/deployment (CI/CD), and operational maintenance • Execute data preprocessing, feature engineering, feature selection, and rigorous model evaluation to ensure data quality and performance • Track deployed models for performance, reliability, and accuracy drift; troubleshoot operational issues; and drive continuous automation improvements • Partner with data scientists, data engineers, and software engineers to integrate AI/ML solutions into broad organizational workflows • Document model development processes, deployment workflows, technical findings, and operational procedures Requirements • 6+ years of relevant experience in Machine Learning Engineering, MLOps, AI Engineering, or a related role • Strong hands-on experience in model training, evaluation, validation, and deployment • Deep operational understanding of MLOps principles, model lifecycle management, and monitoring • Proficiency in Python and standard ML frameworks/libraries • Experience working with data pipelines and integrating ML models into production application stacks • Strong analytical, documentation, and collaborative problem-solving capabilities