Machine Learning (ML) Operations Engineer
Summary
ML Operations Engineer at EastWest Bank (hybrid, Taguig) supporting deployment, automation, and monitoring of production ML models and data pipelines using Databricks, MLflow, and GitLab CI/CD, plus Python and container tooling like Docker and Kubernetes.
About the Job
Location: Taguig
Corporate Title: Junior Officer to Manager
Work Arrangement: Hybrid
Our Enterprise Data team is looking for experienced professionals to join us in Taguig with the role of Machine Learning (ML) Operations Engineer.
In this role, you will support the deployment, automation, monitoring, and continuous improvement of machine learning models and data pipelines using Databricks, ML flow, and CI/CD frameworks, ensuring the reliability and quality of production ML systems. Working closely with the Lead ML Operations Engineer, you will help operationalize data science solutions while building expertise in modern ML Operations practices and platform engineering.
Your dream. Our focus.
East West Banking Corporation (EastWest) is one of the largest universal banks in the Philippines, and is committed to continuously invest in people and in process, product, and service enhancements, and embrace new ideas to enhance the EastWest experience.
We empower our employees to drive their careers and are committed to provide the runway for them to grow. We value teamwork and individual initiative. Join us and be part of a highly engaged team, and a workplace that promotes development and goal attainment.
Whether you're just starting out, or already a seasoned professional, EastWest can help you unleash your potential, and bridge the gap between dream to success.
What the role will entail
- Support the deployment, configuration, monitoring, and day-to-day operations of machine learning platforms built on Databricks and MLflow.
- Package, register, version, and deploy machine learning models to production, ensuring smooth model promotion and release processes.
- Build and maintain CI/CD pipelines using GitLab, including automated testing, validation, and deployment workflows.
- Monitor model performance, data quality, and drift, proactively identifying issues and escalating risks to maintain production reliability.
- Collaborate with Data Scientists and Platform Engineers to operationalize machine learning solutions and deliver production-ready deployments.
- Maintain technical documentation, deployment procedures, and platform standards to support scalable and efficient ML Operations practices.
What we're looking for
- Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, or any related technical field.
- 3 - 5 years of experience in Data Engineering, ML Engineering, or ML Operations, with hands-on experience in model deployment and pipeline automation within production environments.
- Strong knowledge of Databricks, ML flow, model lifecycle management, experiment tracking, model registry, and deployment processes.
- Experience building and maintaining CI/CD pipelines using GitLab or similar tools, with proficiency in Python and machine learning frameworks such as scikit-learn, XGBoost, or PyTorch.
- Familiarity with containerization, orchestration, and workflow automation technologies, including Docker, Kubernetes, and Airflow.
- Understanding of model monitoring, performance management, data drift detection, and ML quality assurance practices.
- Relevant certifications such as Databricks, ML flow, Azure, AWS, or GitLab are an advantage, as is familiarity with data governance, BSP regulations, and ISO 27001 requirements in a banking environment.
What you can expect from joining our team
- Career development and training opportunities
- Competitive salary package and benefits
- Performance-based incentives and recognition programs to reward high-performing individuals
- Opportunity to work with industry experts and be mentored by them
- Defined career progression paths to guide you in your professional growth