Production ML Engineer: Scalable AI for FinTech

Wave; United States; Remote; USD 100,000 - 130,000; Full time

Benefits offered by this job

Bonus Structure

Employer-paid Benefits Plan

Health & Wellness Flex Account

Wellness Days

Paid Holiday Shutdown

Wave Days (extra vacation days in the夏

Job summary

Wave is seeking a Machine Learning Engineer in the United States to design, develop, and deploy foundational AI and ML models. You will build robust pipelines and platforms that support advanced analytics and business intelligence, ensuring ML systems are efficient, reliable, and deeply integrated into Wave's goals.

The role requires 3–5 years of hands-on ML experience with Databricks/Redshift, AWS SageMaker, Airflow, and end-to-end ML lifecycle tools.

Qualifications

• 3–5 years of professional ML engineering experience.
• Proven deployment of models to production environments.
• Experience with Databricks or Redshift.
• Hands-on with AWS SageMaker, Spark/AWS Glue, and IaC (Terraform).
• Experience with Airflow or similar orchestration systems.
• Experience with MLflow, Kubeflow, or SageMaker Feature Store.
• Familiarity with governance (lineage, fairness, privacy) and data cataloging.

Responsibilities

• Develop & Deploy: Hands-on building, training, and deployment of ML models in production.
• Champion Technical Standards: Advocate for coding, testing, and MLOps practices across pipelines.
• Optimize & Scale: Build scalable ML/AI use cases balancing existing models with new systems.
• Partner & Collaborate: Work with risk, product, and software teams to embed ML features.
• Establish Controls & Governance: Ensure model dependability, fairness, privacy; integrate lineage tracking and data protection workflows.
• Track & Evaluate: Build observability for model health and operational metrics.

Skills

ML Engineering

Production Deployment

Communication

FinTech domain

Tools

Databricks

Redshift

SageMaker

Airflow

MLflow

Kubeflow

Terraform

AWS Glue

Job description

Wave is seeking a Machine Learning Engineer in the United States to design, develop, and deploy foundational AI and ML models. You will build robust pipelines and platforms that support advanced analytics and business intelligence, ensuring ML systems are efficient, reliable, and deeply integrated into Wave's goals.

The role requires 3–5 years of hands-on ML experience with Databricks/Redshift, AWS SageMaker, Airflow, and end-to-end ML lifecycle tools.