Lead, Machine Learning Operations Engineer
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What you'll do
- 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.
What they're looking for
- 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.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
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!
You will be responsible for the following:
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.
We are looking for:
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.
What We Offer
Opportunity for career growth and development in the #1 FinTech company in the country Working with a dynamic and highly collaborative team who want to change the game A company that values their people with highly competitive and flexible compensation and benefits package
Company
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