Build and deploy AI and ML models at Wave, focusing on scalable pipelines, production reliability, and modern MLOps practices. As an experienced Machine Learning Engineer, you will develop foundational models and platforms supporting advanced analytics and business intelligence. You will work with AWS, Airflow, MLflow, and curated data warehouses while integrating machine learning into live applications. The work includes technical standards, governance, observability, and collaboration with risk, product, and software stakeholders.
Build, train, and deploy machine learning models into productionDevelop resilient, cost-effective AI and ML use casesImprove coding, testing, MLOps, and pipeline standardsPartner with risk, product,



and software stakeholdersEstablish lineage, fairness, privacy, and model observability controlsRequirements: Three to five years of machine learning engineering experienceProduction model deployment experience with modern data stacksThree years of AWS experience with SageMaker and TerraformProficiency with Airflow or similar orchestration systemsExperience with MLflow, Kubeflow, or SageMaker Feature StoreApply your AWS, MLOps, governance, and production machine learning expertise to foundational systems at Wave. #

📌 Machine Learning Engineer Mlops (Winnipeg)
🏢 Wave HQ
📍 Winnipeg