Senior MLOps Engineer: Scalable, Observed ML Systems

Mynt; Metro Manila; On-site; PHP 800,000 - 1,400,000; Full time

Benefits offered by this job

Career growth

Collaborative team

Competitive compensation

Job summary

GCash is seeking a skilled MLOps Engineer to own the end-to-end monitoring and upkeep of production ML models, ensuring high availability and scalable serving. You will build automated monitoring, CI/CD pipelines, and robust data pipelines to support DevOps for AI-powered services.

You will drive incidents reduction, partner with cross-functional teams to accelerate deployment velocity, and document processes for external stakeholders.

Qualifications

• Bachelor's degree in Computer Science, Data Science, or related field.
• Minimum 2+ years 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 SLAs and lead post-mortems.

Responsibilities

• Own end-to-end monitoring and upkeep of production ML models, track performance and drift, and build automation for monitoring at scale.
• Ensure robust ML model serving with scalable data pipelines, APIs, and GenAI apps; design and maintain observability in pipelines.
• Accelerate deployment velocity by implementing CI/CD pipelines and streamlining end-to-end processes.
• Reduce incident volume by establishing monitoring for model performance, data quality, and uptime, with alerting systems.
• Optimize model serving infrastructure using cloud platforms and containerization (AWS, Docker, Kubernetes).
• Create and maintain external-friendly documentation of MLOps processes and runbooks.

Skills

MLOps

Docker

Kubernetes

Python

CI/CD

AWS

Education

Bachelor's degree in CS/DS

Tools

Git

Terraform

Job description

GCash is seeking a skilled MLOps Engineer to own the end-to-end monitoring and upkeep of production ML models, ensuring high availability and scalable serving. You will build automated monitoring, CI/CD pipelines, and robust data pipelines to support DevOps for AI-powered services.

You will drive incidents reduction, partner with cross-functional teams to accelerate deployment velocity, and document processes for external stakeholders.