Machine Learning/MLOps Engineer

The Machine Learning Engineer will join a newly formed team focused on MLOps and ML Engineering within the data science team. This role is crucial for uplifting the existing ML infrastructure into a robust, scalable, and production-ready system, specifically supporting the increasing needs of the company. The engineer will design, build, and maintain the infrastructure, tools, and processes required for continuous integration, continuous delivery, and continuous training (CI/CD/CT) of machine learning models. This is a highly technical role that will interface with data scientists, data engineers, data architects and software engineers to ensure the reliability and performance of ML systems at scale. You will be responsible for designing, implementing, and maintaining scalable MLOps pipelines for model training, testing, deployment, and monitoring. You will build and manage the cloud infrastructure to host and serve real-time and batch machine learning models, establish robust model monitoring and alerting systems, assist and collaborate with Data Scientists to ensure production readiness of models, develop high-quality, reusable MLOps tooling and best practices, drive automation initiatives, and troubleshoot production issues.

Mid-senior level experience in a Machine Learning Engineer, MLOps Engineer, or similar role. Strong proficiency in Python for data and ML workflows and infrastructure automation tools (e.g. Terraform). Extensive experience with cloud platforms (GCP) for building and deploying ML systems. Deep understanding of containerization technologies and CI/CD pipelines. Solid experience with ML experiment tracking, model registry, and feature store platforms. Experience working with high volume, complex data architectures and real-time processing. Experience with SQL for data access and pipeline integration. A strong foundation in computer science fundamentals, software engineering best practices, and system design. Continuous learning mindset and passion for researching and adopting necessary technologies.