ML Ops Engineer

Summary

Join Zuora's Platform Tech team and help shape the future of monetization as a Machine Learning Engineer. You will design, implement, and maintain production-grade ML systems for mission-critical business decisions and customer experiences. This role focuses on building reliable pipelines, deploying models at scale, enabling experimentation, and creating the infrastructure that makes AI real across Zuora’s product suite. You will productionize ML models, optimize for scale and performance, automate pipelines, monitor and maintain models, and collaborate with scientists and engineers. The ideal candidate has 5+ years of experience in machine learning engineering, proven experience deploying ML models, strong data engineering foundations, and proficiency in Python and SQL. Zuora offers competitive compensation, benefits, and a flexible work environment.

Requirements

• 5+ years of experience in machine learning engineering or applied ML development
• Proven experience deploying ML models to production, maintaining APIs, and building CI/CD pipelines for ML
• Strong foundations in data engineering: ETL, batch/stream processing, and data quality practices
• Hands-on experience with MLOps tools like MLflow, SageMaker, Airflow, or similar
• Proficiency in Python and SQL; familiarity with Java or Spark is a plus
• Experience with infrastructure-as-code (e.g., Terraform) and container orchestration (Kubernetes)
• Familiarity with model monitoring, experimentation, and continuous training workflows
• Bachelor's or Master’s degree in Computer Science, Engineering, or a related technical discipline

Responsibilities

• Productionize ML Models – Deploy models (ML & GenAI) into robust production environments using modern ML infrastructure and MLOps practices
• Optimize for Scale & Performance – Build scalable, low-latency ML services and APIs with observability, testing, and failover mechanisms
• Pipeline Automation – Design and implement automated training, testing, and deployment pipelines using tools like SageMaker Pipelines, Airflow, and MLFlow
• Model Monitoring & Maintenance – Implement monitoring for model drift, data quality, and performance metrics. Own retraining and rollback strategies
• Partner with Scientists & Engineers – Collaborate with data scientists to take notebooks to production, and with software engineers to integrate ML into customer-facing systems
• Champion Best Practices – Define best practices for ML development lifecycle, including CI/CD for models, reproducibility, and secure deployment

Preferred Qualifications

• Experience with GenAI deployment (Bedrock, LangChain, Claude, etc.)
• Familiarity with vector databases (FAISS, Pinecone) and graph databases (Neo4j)
• Exposure to A/B testing or online experimentation platforms
• Understanding of privacy, security, and governance in ML deployments

Benefits

• Competitive compensation, corporate bonus program and performance rewards, company equity and retirement programs
• Medical insurance
• Generous, flexible time off
• Paid holidays, “wellness” days and company wide end of year break
• 6 months fully paid parental leave
• Learning & Development stipend
• Opportunities to volunteer and give back, including charitable donation match
• Free resources and support for your mental wellbeing