Overview
Skills
Job Details
Job Title: Machine Learning Operations (MLOps) Engineer
Location: Scottsdale, AZ (preferred); Chicago, IL; New York, NY
About the Role:
We are seeking a skilled Machine Learning Operations (MLOps) Engineer to bridge the gap between data science, software development, and IT operations. This role applies DevOps principles to streamline the building, deployment, and monitoring of machine learning (ML) models.
Our MLOps team empowers Data Scientists to build and deploy innovative models while developing cutting-edge, cloud-native capabilities. These solutions deliver predictive modeling faster, more accurately, and more efficiently, helping to protect the banking system from fraud and malicious activity.
The MLOps Engineer will support the platforms, tools, and processes that take ML models from concept to production, enabling real-time prediction services. This role partners closely with Data Science, Data Product Management, Product Engineering, and Data Platform teams to automate model productionalization, maintain deployed models, and ensure reliability and performance in production environments.
Key Responsibilities:
Collaborate with Data Science, Product, and Engineering teams to deploy ML models into production.
Support and maintain ML models in production environments, ensuring high availability and performance.
Automate model productionalization workflows, from versioning and testing to deployment and monitoring.
Generate synthetic or test data to validate model performance before production deployment.
Monitor, troubleshoot, and optimize ML pipelines and services.
Implement DevOps best practices for ML environments and cloud-native architecture.
Support Data Science teams with operationalizing models and scaling predictive solutions.
Required Qualifications:
Strong experience with AWS services (certification preferred).
Proficient in Python and SQL; experience with Spark or PySpark.
Proven experience deploying ML models into production (must have production deployment experience).
Experience with MLOps or ML engineering practices, including model monitoring and maintenance.
Familiarity with DevOps principles, CI/CD pipelines, and cloud-native ML workflows.
Ability to work cross-functionally with Data Science, Engineering, and Platform teams.
Preferred Qualifications:
AWS Certification (Solutions Architect, Machine Learning Specialty, or equivalent).
Experience with containerization (Docker, Kubernetes) in ML deployments.
Knowledge of ML testing frameworks and synthetic data generation.
Strong problem-solving, communication, and collaboration skills.
Why Join Us:
Work on cutting-edge ML solutions that protect the banking system from fraud and abuse.
Collaborate with talented teams of Data Scientists, Engineers, and Product leaders.
Opportunity to design and implement scalable, cloud-native ML platforms.