Machine Learning Engineer developing production AI models and scalable MLOps platforms for Wave, which helps small businesses thrive.
Collaborating on financial-risk applications, governance, observability, and reliable deployment.
Responsibilities
Design, develop, train, and deploy foundational AI and machine learning models in production environments
Build robust, scalable machine learning pipelines and platforms supporting advanced analytics and business intelligence
Advocate for high standards across coding, testing, and MLOps processes
Construct resilient, cost-efficient ML and AI use cases and scale modern systems
Collaborate with risk specialists, product leads, and software developers to translate strategic needs into technical specifications and embed ML features into live applications
Establish model dependability, fairness, compliance, lineage tracking, and data protection controls
Develop observability systems to capture model health and operational metrics and evaluate organizational value
Requirements
Minimum 3–5 years of professional experience in machine learning engineering
Proven track record of deploying machine learning models into production environments
Deep understanding of the modern data stack and data ingestion workflows
Experience with Databricks or Redshift
At least 3 years of hands-on experience with AWS infrastructure, including SageMaker, Spark/AWS Glue, and Terraform
High proficiency with Airflow or similar orchestration systems
Practical experience with MLflow, Kubeflow, or SageMaker Feature Store
Familiarity with model governance practices, including lineage, fairness, and privacy
Experience using data cataloging tools for compliance
Strong ability to communicate complex technical concepts to non-technical stakeholders and influence project direction
FinTech or Financial Risk experience is a significant advantage
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