Machine Learning Operations Engineer

Internal Role Title: Senior ML Ops Engineer

Work setup: We operate in a hybrid work environment, and we encourage applicants who are open to working in the office two days a week to apply.

Work schedule: Monday to Friday, 10AM to 6PM Manila time, overlaps with UK operating hours

Employment type: Permanent

Location: Makati City, Metro Manila

Discover a world of endless possibilities with Cambridge University Press & Assessment, a distinguished global academic publisher and assessment organization proudly affiliated with the prestigious University of Cambridge.

We are looking for a dedicated Machine Learning Operations Engineer to join our team. The ideal candidate will be responsible for designing, implementing, and maintaining scalable, automated production systems for our machine learning models.

Why Cambridge?

Cambridge University Press & Assessment is a world-renowned not-for-profit academic publisher and assessment organisation, proudly part of the prestigious University of Cambridge. With a legacy rooted in over 800 years of educational excellence, we are dedicated to unlocking the potential of learners and educators across the globe.

Joining Cambridge's second largest global office in the Philippines —operating for over 22 years with 1,300+ colleagues— means becoming a part of an extraordinary institution renowned worldwide. We are recognised as a Great Place to Work® for three consecutive years, reflecting our inclusive culture, strong sense of purpose, and commitment to the professional growth and well-being of our people. At Cambridge, we don't just publish books or deliver tests—we empower progress, inspire curiosity, and champion the pursuit of knowledge.

What can you get from Cambridge?

At Cambridge, you'll become a part of a vibrant and forward-thinking community that transcends tradition, fostering a culture of continuous growth and personal development. Here, we provide the right environment for you to thrive, supporting your professional journey and empowering you to reach your highest potential, that is why our pay philosophy is intricately tied to your skills and competencies, ensuring that your compensation aligns with the unique value you bring to the role you are applying for.

The organization offers a wide range of benefits and opportunities including:

• Regular Employment on Day 1
• HMO Coverage and Life Insurance on Day 1
• Paid Annual Leaves (Vacation, Well-being, Flexible, Holiday, and Volunteering leaves)
• Vesting/Retirement package
• Opportunities for career growth and development
• Access to well-being programs
• Flexible schedule, hybrid work arrangement and work-life balance
• Opportunity to collaborate with colleagues from diverse branches that will expand your horizons and enrich your understanding of different cultures

What will you do as a Machine Learning Operations Engineer? 

Reporting to the AI/ML Technical Lead, your accountabilities will include:

• Architect, implement, and govern end-to-end scalable, automated ML pipelines for training, deployment, and monitoring.
• Lead the strategy for ML Ops infrastructure, including IaC (Terraform), containerization (Docker/Kubernetes), and CI/CD pipelines (GitHub Actions).
• Manage the lifecycle of ML workloads on AWS cloud infrastructure, focusing on cost, security, and reproducibility.
• Establish and manage comprehensive observability standards and tooling (like Prometheus, Grafana, Sentry) to ensure system reliability.
• Serve as the primary technical liaison for data scientists and engineers, championing and embedding MLOps best practices across the organization

Please review the attached job description for further details on the role. 

What makes you the ideal candidate for this role?

An ideal candidate has the following qualities:

 
Essential:

• With 2–5 years of DevOps experience (highly preferably with AI/ML development), with a strong understanding of MLOps principles and Machine Learning lifecycle management.
• Proven experience with Machine Learning model deployment and monitoring in production environments.
• Strong proficiency in Python and core MLOps infrastructure technologies: Terraform, Docker, Kubernetes, and AWS services.
• Extensive experience with ML frameworks (like TensorFlow, PyTorch, scikit-learn).
• Familiarity with the broader MLOps ecosystem, including orchestration (i.e. Airflow/Metaflow) , experiment tracking (MLflow/W&B) , serving (Nvidia Triton, Ray Serve, etc.) and CI/CD (GitHub Actions)

For the selection process, note that there may be a technical interview to assess your skills that can include a take home task.

Are you driven by desire to be part of a globally renowned institution that celebrates innovation, embraces inclusion, and empowers learners? Then, we invite you to Pursue your Potential with us.

Applications received through the system will be reviewed on a rolling basis and may close the vacancy once sufficient applications are received. Therefore, if you are interested, tailor-fit your CV (advantageous if you submit one with a Cover Letter) and submit as early as possible.

NOTE: Feedback and interview updates may be shared starting January. We appreciate your patience and understanding as we complete the review process.

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