We re looking for a proactive and hands-on MLOps Engineer to join our R D team and drive internal innovation In this role you will work closely with software engineers to research and operationalize cutting-edge models and build robust infrastructure that accelerates experimentation and enables scalable deployment of ML solutions Your primary mission will be to build operationalize and maintain machine learning models that help our teams extract insights from data faster and develop proof-of-concepts more efficiently across departments This is a unique opportunity to be at the center of internal experimentation developing reusable ML components automating analysis workflows and turning raw data into powerful internal tools that accelerate decision-making Collaborate with analysts product managers and engineers to understand internal data needs and POC requirements Design build and maintain scalable MLOps infrastructure CI CD model versioning orchestration monitoring Create reusable pipelines and tools for faster model development and experimentation Streamline and automate the process of generating visualizations dashboards and reports using ML-based techniques Set up infrastructure and practices for model tracking versioning and reproducibility e g MLflow DVC Ensure reproducibility and traceability of experiments and models across environments Monitor and troubleshoot model performance in staging and production environments Manage and optimize GPU compute environments data storage solutions and ML model serving layers Implement testing validation and rollback mechanisms for ML models Stay current with MLOps trends and best practices introduce tools and practices that improve team productivity Support PoC projects with rapid prototyping and deployment of smart data solutions Qualifications Bachelor s or Master s degree in Computer Science Engineering or a related field 3 years of experience in DevOps MLOps or infrastructure engineering in a production setting Proficiency with cloud platforms AWS GCP or Azure and containerization Docker Kubernetes Strong experience with Python and ML frameworks e g TensorFlow PyTorch Scikit-learn Familiarity with ML lifecycle tools like MLflow DVC Weights and Biases etc Solid understanding of data pipelines version control and CI CD systems e g GitHub Actions Jenkins Nice-to-Have Experience working in an R D or experimental setting Exposure to distributed training model compression or edge deployment Experience building internal tools dashboards or data products used by non-technical teams Ability to communicate ML concepts clearly to non-technical stakeholders Comfortable in fast-paced iterative environments with shifting priorities Additional Information Please be aware that job-seekers may be at risk of targeting by scammers seeking personal data or money Nielsen recruiters will only contact you through official job boards LinkedIn or email with a nielsen com domain Be cautious of any outreach claiming to be from Nielsen via other messaging platforms or personal email addresses Always verify that email communications come from an nielsen com address If you re unsure about the authenticity of a job offer or communication please contact Nielsen directly through our official website or verified social media channels