Build and deploy AI and ML models at Wave, focusing on scalable pipelines, production reliability, and modern 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-effective 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.

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