About the Role

In this role, you will focus on MLOps, 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 Do

• Collaborate 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 tooling
• Design, 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 efficiency
• Contribute to the roadmap for Machine Learning Engineering and Data Science tools, including developing reusable frameworks and standardized solutions to streamline model implementation
• Partner with and support Data Scientists by enabling effective use of cloud-based tools and infrastructure, and providing technical expertise across the ML lifecycle
• Collaborate with machine learning engineers to share knowledge, improve best practices, and foster a culture of continuous learning and development
• Support development and maintain monitoring, alerting, and automated testing frameworks to ensure the reliability, performance, and integrity of data pipelines, models, and infrastructure
• Develop, document, and communicate implementations and best practices across the data science lifecycle
• Manage and communicate cloud infrastructure costs and budgets to project stakeholders
• Stay current with GCP services and evolving best practices in Machine Learning Engineering and MLOps
• Additional tasks may be assigned

What Skills You Have

Required

• Experience 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 frameworks
• Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics or equivalent quantitative field
• Experience working with Data Scientists to deploy, scale, and operationalize machine learning models in production environments
• 3+ years of experience as a Machine Learning Engineer with a proven track record of successful project delivery
• In-depth knowledge of cloud platform, preferably Google Cloud Platform services, particularly Vertex AI, BigQuery and Dataproc.
• Extensive expertise with CI/CD and IaC best practices
• Extensive knowledge of distributed computing and big data technologies like Spark, Kubeflow, Airflow and SQL
• Extensive 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 delivery

Preferred

• Master’s Degree
• Proficiency in Java or other languages
• Retail experience
• E-commerce experience
• 5+ years of experience in Machine Learning
• Experience 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 agents