- Experience: Minimum of 3–5 years of professional experience in machine learning engineering, with a proven track record of deploying models into production environments.
- Technical Depth: Deep understanding of the modern data stack, including data ingestion workflows and experience working with curated data warehouses like Databricks or Redshift.
- Cloud Proficiency: At least 3 years of hands-on experience with AWS infrastructure, specifically SageMaker, Spark/AWS Glue, and Infrastructure as Code (IaC), Terraform.
- Orchestration Expert: High proficiency in managing multi-stage workflows using Airflow or similar orchestration systems to automate training and deployment cycles.
- MLOps Toolkit: Practical experience with MLflow, Kubeflow, or SageMaker Feature Store to support the end-to-end machine learning lifecycle.
- Governance Mindset: Familiarity with model governance practices (lineage, fairness, and privacy) and experience using data cataloging tools for compliance.
- Communication: Strong ability to communicate complex technical concepts to non-technical stakeholders and influence project direction.
- Industry Context: Experience in FinTech or Financial Risk environments is a significant advantage.
At Wave, we value diversity of perspective. Your unique experience enriches our organization. We welcome applicants from all backgrounds. Let’s talk about how you can thrive here!
Wave is committed to providing an inclusive and accessible candidate experience. If you require accommodations during the recruitment process, please let us know by emailing careers@waveapps.com. We will work with you to meet your needs.
We use Google Gemini, a secure AI assistant, during interviews for note-taking purposes only. Notes are kept confidential and are not shared outside the hiring process. This allows our interviewers to stay fully focused on you during the conversation.
This advertised posting is a current vacancy.