AI/ML Engineering Manager

Chinabank PH; Philippines; On-site; PHP 1,800,000 - 2,600,000; Full time

Job summary

Chinabank PH seeks an AI/ML Engineering Manager to lead a team responsible for building, deploying, and scaling AI and ML solutions in a banking context. You will blend hands-on technical leadership with people management to deliver production-ready models and scalable ML platforms.

You will partner with data science, data engineering, IT, and architecture teams to translate research into secure, scalable deployments across cloud, on-premise, and hybrid environments while mentoring engineers and

Qualifications

• Bachelor's degree in a relevant field (CS/DS/Math/Engineering).
• 5+ years in ML engineering, data science, or software engineering.
• 2+ years leading or managing technical teams.
• Experience deploying ML models in production environments.

Responsibilities

• Lead day-to-day AI/ML engineering operations, including sprint planning and delivery management.
• Guide engineers in building, deploying, and maintaining ML models and AI applications.
• Oversee feature engineering pipelines, deployment workflows, and enterprise integrations.
• Drive engineering best practices through code reviews, testing, and documentation.
• Collaborate with Data Science teams to turn research into production-ready solutions.
• Build and maintain ML deployment pipelines, automation processes, and platform reliability.
• Work with IT, architecture, and infra teams to support cloud, on-premise, and hybrid environments.
• Monitor model performance, deployment stability, and operational readiness.
• Mentor AI/ML engineers and foster a culture of innovation and learning.

Skills

Python

Leadership

Stakeholder management

Agile delivery

Mentoring

Education

Bachelor's degree in Computer Science or related field

Tools

Docker

Kubernetes

CI/CD

Git

TensorFlow

PyTorch

Scikit-learn

Job description

Are you a hands-on engineering leader who enjoys building AI solutions that deliver real business value? Do you thrive in fast-paced environments where you can lead teams, solve complex technical challenges, and turn innovative ideas into production-ready applications?

We're looking for an AI/ML Engineering Manager to lead the engineering team responsible for building, deploying, and scaling AI and machine learning solutions. In this role, you'll combine strong technical expertise with people leadership, ensuring high-quality delivery while mentoring engineers and driving engineering excellence.

You'll work closely with data scientists, data engineers, IT teams, architects, and business stakeholders to transform AI models into reliable, secure, and scalable solutions that support business objectives.

What You Will Be Doing

• Lead day-to-day AI/ML engineering operations, including sprint planning, task prioritization, and delivery management.
• Guide engineers in building, deploying, and maintaining machine learning models and AI applications.
• Oversee the development of feature engineering pipelines, model deployment workflows, and enterprise integrations.
• Drive engineering best practices through code reviews, testing standards, documentation, and technical mentoring.
• Collaborate with Data Science teams to convert research prototypes into production-ready solutions.
• Build and maintain ML deployment pipelines, automation processes, and platform reliability standards.
• Work with IT, architecture, and infrastructure teams to support cloud, on-premise, and hybrid environments.
• Monitor model performance, deployment stability, and operational readiness.
• Manage technical risks, resolve delivery challenges, and ensure solutions are delivered on time and within scope.
• Mentor and develop AI/ML Engineers while fostering a culture of innovation, collaboration, and continuous learning.

What Success Looks Like

In this role, success means:

• AI and machine learning solutions are delivered on schedule and perform reliably in production.
• Engineering teams consistently meet quality, performance, and delivery expectations.
• Models and pipelines are scalable, maintainable, and aligned with enterprise standards.
• Automated deployment and MLOps practices improve efficiency and reduce operational risks.
• Technical documentation, governance artifacts, and operational handoffs are complete and audit-ready.
• Team members continue to grow their skills and contribute to a culture of engineering excellence.
• Stakeholders view the engineering team as a trusted partner in delivering innovative AI solutions.

Qualifications

Required

• Bachelor's degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related field.
• At least 5 years of experience in Machine Learning Engineering, Data Science, or Software Engineering.
• At least 2 years of experience leading or managing technical teams.
• Experience building and deploying machine learning models in production environments.
• Strong hands‑on programming experience using Python and modern machine learning frameworks.
• Experience with data engineering, data pipelines, and feature engineering.
• Knowledge of cloud platforms and modern software development practices.
• Experience working with Agile delivery teams and sprint-based development.
• Strong leadership, communication, and stakeholder management skills.

Preferred

• Experience in banking, financial services, fintech, or other highly regulated industries.
• Hands‑on experience with TensorFlow, PyTorch, Scikit-learn, or similar ML frameworks.
• Experience with MLOps tools and practices, including model versioning, automation, and monitoring.
• Familiarity with Azure, AWS, or GCP cloud environments.
• Experience with Docker, Kubernetes, CI/CD pipelines, and Git-based development workflows.
• Knowledge of Responsible AI, model governance, and regulatory compliance requirements.

What You Can Expect Here

• Opportunities to lead high-impact AI and machine learning initiatives.
• Exposure to cutting‑edge technologies, cloud platforms, and modern engineering practices.
• Collaboration with data scientists, engineers, architects, and business leaders.
• A culture that encourages innovation, technical excellence, and continuous learning.
• Opportunities to mentor, grow teams, and influence the future direction of AI engineering.
• Meaningful work that helps transform how AI is applied across the organization.