Build and deploy AI and ML models at Wave, focusing on scalable pipelines, production reliability, and contemporary MLOps practices. Work wherever you are most successful and connected.
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.
Key Responsibilities: Build, train, and deploy machine learning models into production
Develop resilient, cost-efficient AI and ML use cases
Improve coding, testing, MLOps, and pipeline standards
Partner with risk,



product, and software stakeholders
Establish lineage, fairness, privacy, and model observability controls
Requirements: Three to five years of machine learning engineering experience
Production model deployment experience with modern data stacks
Three years of AWS experience with SageMaker and Terraform
Proficiency with Airflow or similar orchestration systems
Experience with MLflow, Kubeflow, or SageMaker Feature Store
Apply your AWS, MLOps, governance, and production machine learning expertise to foundational systems at Wave.

#J-18808-Ljbffr

📌 Machine Learning Engineer MLOps (Manitoba)
🏢 Wave HQ
📍 Manitoba