Machine Learning Engineer

We’re looking for a hands-on Contract Software Engineer to join a Machine Learning Platform team focused on building the cloud infrastructure and platform tooling that enables data scientists and ML engineers to deliver production-ready workflows. This is a platform-first role, centered on MLOps, cloud-native systems, and backend services on Google Cloud Platform (GCP). You won’t own individual ML models, instead you’ll help create the scalable, secure foundation that powers them. If you enjoy building developer platforms, enabling ML at scale, and solving real-world infrastructure problems, this role is for you. Must Have • Google Cloud Platform (GCP) • MLOps experience What You’ll Do • Build platform services, SDKs, APIs, and tooling that support ML workflows in production • Design and operate cloud-native infrastructure on GCP • Develop backend services to standardize MLOps patterns and deployment workflows • Translate architecture into secure, reliable implementations • Partner with data science and engineering teams to enable repeatable ML processes • Troubleshoot platform and infrastructure issues with an automation-first mindset • Implement monitoring, logging, and alerting for ML platform services • Improve performance, scalability, reliability, and cost efficiency • Create technical documentation and usage guidelines • Support internal users and guide teams on ML platform best practices Required Qualifications • Strong proficiency in Java, Python, or similar languages • Hands-on experience with GCP (compute, storage, networking, IAM, Vertex AI) • Professional experience building platform, infrastructure, or backend systems • Demonstrated MLOps experience • Strong API and backend service development background • Experience with Docker and containerized environments • Familiarity with CI/CD pipelines and Git-based workflows • Working knowledge of Terraform and networking concepts • Strong problem-solving skills and ability to work independently Nice to Have • Experience with Vertex AI or managed ML platforms • Familiarity with model deployment, monitoring, and lifecycle patterns • Experience building reusable infrastructure or developer platforms • Knowledge of relational and NoSQL databases • Experience operating distributed systems in production