Lead MLOps Engineer: Production AI & CI/CD

GCash; Metro Manila; On-site; PHP 1,200,000 - 2,000,000; Full time

Benefits offered by this job

Career growth

Competitive compensation

Collaborative team

Job summary

GCash is seeking an MLOps Engineer to own end-to-end monitoring of production ML models, oversee deployment and performance tuning, and build scalable CI/CD pipelines. You will ensure high availability of model serving and collaborate across teams to optimize infrastructure on AWS, Docker, and Kubernetes.

The role requires a Bachelor's in CS/Data Science and at least 2 years in MLOps or related fields, with a focus on reliability, observability, and documentation.

Qualifications

• Bachelor’s degree in Computer Science, Data Science, or related field.
• Minimum 2+ years of hands-on experience in MLOps, DevOps, Data Engineering, or Software Engineering.
• Proven expertise in ML Operations (MLOps), deployment, monitoring and tuning.
• Experience in triaging incidents, post-mortems, and problem management.

Responsibilities

• Own end-to-end monitoring and upkeep of production ML models, including performance and drift.
• Design, build, and maintain scalable ML Ops pipelines and model serving with high availability.
• Collaborate to implement automated CI/CD for ML models and streamline processes.
• Reduce incident volume by monitoring data quality, pipelines, and uptime with alerts.
• Optimize model serving infrastructure on cloud and with containerization (AWS, Docker, Kubernetes).
• Create and maintain external-facing documentation of MLOps processes and runbooks.

Skills

MLOps

Python

Docker

Kubernetes

CI/CD pipelines

AWS Cloud

Data Engineering

DevOps

Education

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

Tools

Docker

Kubernetes

AWS

Job description

GCash is seeking an MLOps Engineer to own end-to-end monitoring of production ML models, oversee deployment and performance tuning, and build scalable CI/CD pipelines. You will ensure high availability of model serving and collaborate across teams to optimize infrastructure on AWS, Docker, and Kubernetes.

The role requires a Bachelor's in CS/Data Science and at least 2 years in MLOps or related fields, with a focus on reliability, observability, and documentation.