MLOps Engineer / Model Deployment Engineer / Machine Learning Platform Engineer

Role Description Builds and maintains the infrastructure and processes that help machine learning models move from development into reliable production use. Works with data scientists, machine learning engineers, software engineers, and platform teams to create repeatable workflows for preparing data, training and validating models, managing versions, deploying services, and monitoring performance. Develops automation for machine learning pipelines, including data and feature processing, model training, testing, packaging, release, and rollback. Helps manage model artifacts, metadata, dependencies, configurations, and access across development, testing, and production environments. Designs deployment approaches suited to the use case, such as batch processing, real-time APIs, or edge deployment, while taking performance, reliability, security, and cost into account. Monitors production models and supporting services for availability, latency, errors, resource use, data changes, and model performance. Helps detect issues such as data drift, changes in prediction quality, pipeline failures, or infrastructure problems, and works with relevant teams to investigate and resolve them. Supports practices for model governance, auditability, reproducibility, testing, and controlled releases. Maintains the machine learning platform and developer tools used by teams to build and operate models. This may include cloud infrastructure, container and orchestration systems, model registries, feature stores, experiment tracking, workflow schedulers, and observability tools. Improves the platform through automation, documentation, reusable components, and feedback from users. For organizations deploying generative AI, the role may also support model serving, evaluation pipelines, prompt or configuration versioning, and monitoring of AI application behavior. Qualifications * Bachelor’s degree in computer science, software engineering, data engineering, or a related field; equivalent experience may be considered * Experience in MLOps, machine learning engineering, platform engineering, DevOps, or production software operations * Proficiency in Python and familiarity with software development practices such as version control, testing, code review, and documentation * Understanding of machine learning workflows, model artifacts, training and inference, and deployment considerations * Experience building or operating automated pipelines for data, model training, validation, or deployment * Familiarity with cloud platforms such as AWS, Azure, or Google Cloud * Experience with containers and orchestration tools such as Docker and Kubernetes is an asset * Knowledge of CI/CD, infrastructure as code, workflow orchestration, and monitoring tools is beneficial * Familiarity with model registries, feature stores, experiment tracking, or model serving frameworks is an asset * Strong troubleshooting skills and ability to diagnose issues across applications, data pipelines, and infrastructure * Understanding of reliability, access control, security, and cost management in production systems * Ability to collaborate with data scientists and engineers and communicate platform constraints clearly * Good documentation skills and a practical approach to building reusable, maintainable systems * Experience with model monitoring, drift detection, reproducibility, or regulated deployment environments is beneficial * Relevant cloud, Kubernetes, DevOps, or machine learning certifications are an asset