Machine Learning Engineer MLOps

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-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.

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