Machine Learning Operations Lead For Pooling

UnionBank; Pasig; On-site; PHP 2,800,000 - 5,600,000; Full time

Job summary

UnionBank is seeking an experienced MLOps Lead to drive end‑to‑end operationalization of machine learning solutions. You will design and implement ML pipelines, CI/CD workflows, feature stores, and deployment frameworks, collaborating with Data Science, Data Engineering, and DevOps teams to ensure scalable and secure ML systems.

The role emphasizes governance, automation, and reliable model delivery to accelerate business outcomes.

Qualifications

• Expertise in Python and software engineering fundamentals.

Responsibilities

• Lead the design, development, and maintenance of end‑to‑end machine learning pipelines, ensuring reliability, scalability, and production readiness.

Skills

Python

SQL

MLflow

Kubeflow

SageMaker

Vertex AI

CI/CD

GitOps

Docker

Kubernetes

AWS

Azure

Snowflake

Job description

Job Description:

Machine Learning Operations Lead

Office Location: UnionBank Plaza, Ortigas, Pasig City

Employment Type: Full-time

About The Role

The MLOps Lead is responsible for driving the end‑to‑end operationalization of machine learning solutions, ensuring they are scalable, secure, and production‑ready. This role provides technical leadership in designing and implementing ML pipelines, CI/CD workflows, feature management, and model deployment frameworks. The MLOps Lead collaborates closely with Data Science, Data Engineering, and DevOps teams to establish standards, streamline model delivery processes, and maintain high‑performance ML systems. By overseeing automation, infrastructure, and governance across the ML lifecycle, the role enables the organization to accelerate model development, improve system reliability, and achieve strategic business outcomes.

What You'll Do

• Lead the design, development, and maintenance of end‑to‑end machine learning pipelines, ensuring reliability, scalability, and production readiness.
• Oversee the deployment, monitoring, and ongoing optimization of machine learning models in production environments.
• Build, enhance, and govern CI/CD workflows to automate model training, testing, and deployment processes.
• Guide the implementation and management of feature stores to support efficient feature engineering and reuse across ML projects.
• Collaborate closely with DataOps, Data Engineering, and DevOps teams to ensure seamless data ingestion, processing, storage, and ML system integration.
• Drive infrastructure improvements involving containerization, orchestration, and cloud‑based ML environments using tools such as Docker, Kubernetes, AWS, and Azure.
• Ensure ML solutions comply with organizational standards for security, reliability, scalability, and operational governance.
• Provide technical leadership, mentorship, and architectural guidance to the ML engineering team, promoting best practices and operational excellence.
• Troubleshoot and resolve performance, integration, and production issues related to ML systems.

What We're Looking For

• Expertise in Python and strong software engineering fundamentals. Strong knowledge of SQL and experience with large‑scale databases.
• Deep hands‑on experience with MLOps frameworks (MLflow, Kubeflow, SageMaker, Vertex AI, or similar).
• Advanced skills in DevOps: CI/CD, GitOps, Docker, Kubernetes.
• Solid experience deploying and monitoring ML models in production environments.
• Strong working knowledge of cloud environments (AWS or Azure).
• Proficiency in Snowflake and integrating data platforms with ML p