Senior Machine Learning Ops Engineer

Avanciers is seeking a highly skilled Senior Machine Learning Ops Engineer to join our team for an exciting Fulltime role with one of our esteemed Fortune 500 clients, based in Canada. This is a Remote role.Position: Senior Machine Learning Ops EngineerLocation: Canada- RemoteJob Description:Job Overview:We are seeking an experienced and highly skilled MLOps Engineer to design, scale, and support our Google Vertex AI-based machine learning platform, enabling a multi-model serving environment. This role focuses on building reusable infrastructure components, developing model deployment pipelines, and providing automation templates that streamline deployment, monitoring, and lifecycle management for domain teams.You will collaborate closely with data scientists, MLOps engineers, and product teams to ensure efficient, observable, and maintainable model deployment aligned with organizational goals. Your contributions will empower domain teams to independently deploy, monitor, and manage their models using standardized platform tools and best practices.This position is part of a broader ML Platform Team supporting product recommendation capabilities, working in close partnership with data engineers, machine learning engineers, and software developers to build and maintain robust data pipelines, ML models, and backend services.Key ResponsibilitiesDesign and implement reusable modules and templates for model training, deployment, and monitoring using Vertex AI and other cloud technologies.Develop and maintain scalable CI/CD pipelines for machine learning workflows to enable rapid iteration and safe promotion across environments.Build tooling and automation to enhance platform observability—covering model drift detection, performance metrics, latency tracking, and alerting.Collaborate with domain teams to onboard models into the platform, ensuring compliance with operational standards and SLAs.Maintain and evolve feature stores, model registries, and endpoint management systems for high-throughput, low-latency inference environments.Define and enforce governance policies including version control, rollback procedures, and access management.Provide technical leadership and guidance to enable self-service model operations and reduce platform dependencies.Partner with data scientists to streamline the integration of models into production systems.Act as a bridge between data science, data engineering, MLOps, and leadership teams to ensure alignment and effective communication.QualificationsBachelor’s or Master’s degree in Computer Science, Engineering, or a related field.4+ years of experience in MLOps or machine learning infrastructure roles.Strong proficiency in Python and familiarity with modern data science and ML practices.Hands-on experience with Google Cloud (GCP), particularly Vertex AI, and adaptability to Azure ML, AWS SageMaker, or open-source equivalents.Expertise in ML frameworks such as TensorFlow, PyTorch, and scikit-learn, including integration via custom containers.Proven ability to design scalable, maintainable, and secure ML systems.Excellent communication and stakeholder management skills, with the ability to translate between technical and business domains.Preferred SkillsDemonstrated experience collaborating closely with data science and analytics teams.Strong problem-solving, documentation, and process improvement skills.Experience leading platform development initiatives and mentoring junior engineers.Familiarity with a wide range of ML tools, orchestration systems, and open-source technologies.Willingness to adapt and learn emerging technologies in the ML ecosystem.