About the RoleAs Senior MLOps Engineer, you will focus on supporting cross-functional teams in designing, deploying, and operating machine learning solutions while building scalable infrastructure, tools, and best practices across the Machine Learning Engineering (MLE) ecosystem.What You’ll DoCollaborate with Data Scientists and Engineers across the full ML lifecycle, including building and scaling ETL pipelines, deploying models into customer-facing applications, and enabling efficient model development through cloud infrastructure and toolingDesign, build, and maintain scalable machine learning infrastructure, including model serving (real-time and batch), training environments, and orchestration systems, with a focus on performance, scalability, and cost efficiencyContribute to the roadmap for Machine Learning Engineering and Data Science tools, including developing reusable frameworks and standardized solutions to streamline model implementationPartner with and support Data Scientists by enabling effective use of cloud-based tools and infrastructure, and providing technical expertise across the ML lifecycleCollaborate with machine learning engineers to share knowledge, improve best practices, and foster a culture of continuous learning and developmentSupport development and maintain monitoring, alerting, and automated testing frameworks to ensure the reliability, performance, and integrity of data pipelines, models, and infrastructureDevelop, document, and communicate implementations and best practices across the data science lifecycleManage and communicate cloud infrastructure costs and budgets to project stakeholdersStay current with GCP services and evolving best practices in Machine Learning Engineering and MLOpsAdditional tasks may be assignedWhat Skills You HaveRequiredExperience in MLOps or DevOps practices, including building and operating production ML systems using Docker, Kubernetes, CI/CD pipelines, Git-based version control, API development, model serving (batch and real-time), and automated testing frameworksBachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics or equivalent quantitative fieldExperience working with Data Scientists to deploy, scale, and operationalize machine learning models in production environments3+ years of experience as a Machine Learning Engineer with a proven track record of successful project deliveryIn-depth knowledge of cloud platform, preferably Google Cloud Platform services, particularly Vertex AI, BigQuery and Dataproc.Extensive expertise with CI/CD and IaC best practicesExtensive knowledge of distributed computing and big data technologies like Spark, Kubeflow, Airflow and SQLExtensive expertise in Python and machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn)Experience working in Agile environments with an emphasis on iterative development and continuous deliveryPreferredMaster’s Degree Proficiency in Java or other languagesRetail experienceE-commerce experience5+ years of experience in Machine LearningExperience with optimization techniques and tools (e.g., Gurobi, linear programming, mixed-integer programming)Experience working with agent based or agentic AI systems, including orchestration of autonomous workflows or LLM-driven agentsJob SummaryJob number: R472925Profession: Technology
