Lead, Machine Learning Operations Engineer

Do you want to take the first step in making Filipinos’ lives better everyday? Here in GCash we want to stay at the forefront of the FinTech industry by creating innovative, meaningful, and convenient financial solutions for the nation! G ka ba? Join the G Nation today!

• Own the end-to-end, continuous monitoring and healthy upkeep of production machine learning models, tracking performance and drift; diagnose and debug issues across model deployment, runtime performance, data pipelines, and infrastructure. Build the automations and platforms required to scale model monitoring.

• Ensure robust, high-availability ML model serving (i.e. data pipelines, model APIs, GenAI Apps) by designing, building, and maintaining scalable and observable ML Operations pipelines.

• Accelerate model deployment velocity by collaborating cross-functionally to implement and optimize automated CI/CD pipelines, and streamline end to end process.

• Proactively reduce incident volume and maintain target SLAs by establishing comprehensive model performance, data quality, and pipeline uptime monitoring and alerting systems.

• Drive cost efficiency and performance improvements by optimizing model serving infrastructure using cloud platforms and containerization (AWS, Docker, Kubernetes)

• Establish institutional knowledge and compliance by creating and maintaining clear, external-friendly documentation of MLOps processes.

• Bachelor’s degree in Computer Science, Data Science, or related field

• Minimum 2+ years of hands-on experience in a production environment covering MLOps, DevOps, Data Engineering, or Software Engineering

• Proven expertise in ML Operations (MLOps), specifically model deployment, proactive monitoring, and performance tuning

• Proven capability to triage and resolve production incidents within agreed-upon SLAs, lead post-mortems, and execute Problem Management (Root Cause Analysis) to eliminate recurring operational issues.

• Strong proficiency in containerization and orchestration, specifically Docker and Kubernetes

• Experience utilizing cloud platforms (e.g., AWS Cloud) to host and optimize model serving infrastructure

• Proficiency in core programming languages, especially Python for utility creation, infrastructure automation, health checks, and task automation

• Experience designing and implementing CI/CD pipelines for machine learning models

• Demonstrated capability to meet and exceed stringent Service Level Agreements (SLAs), particularly those related to model uptime and incident resolution

• Demonstrated experience in building, maintaining, and curating technical knowledge repositories, operational runbooks, standard operating procedures (SOPs), and model cards.