MLops Engineer

DYSON OPERATIONS PTE. LTD.; Singapore; On-site; SGD 50,000 - 70,000; Full time

Job summary

A leading technology company in Singapore is seeking an entry-level MLOps Engineer. The role involves developing and deploying machine learning models, managing cloud infrastructure on Google Cloud Platform, and collaborating with various stakeholders. Ideal candidates should have strong technical skills, a problem-solving mindset, and an eagerness to learn in the MLOps domain.

Qualifications

• Entry-level role focusing on MLOps, supporting machine learning model development and deployment.
• Hands-on experience with CI/CD pipelines and cloud services.
• Ability to collaborate with technical and non-technical stakeholders.

Responsibilities

• Build automated workflows for MLOps processes.
• Manage cloud infrastructure on GCP using Infrastructure as Code (IaC).
• Support the deployment and monitoring of machine learning models.

Skills

Collaborative teamwork

Effective communication

Problem-solving

CI/CD pipelines

Machine learning models

Cloud services proficiency

Tools

Google Cloud Platform (GCP)

Docker

Job description

Job Description

The MLOps Engineer is an entry-level role that supports the development and deployment of machine learning models. You will gain hands‑on experience with Cl/CD pipelines, cloud services, and containerization, guided by seasoned MLOps Engineers.

Your tasks will include building automated workflows, integrate version control systems, and deploy models on platforms like Google Cloud Platform (GCP). You'll also manage cloud infrastructure and learn best practices in MLOps.

This role emphasizes both technical skills and soft skills like collaboration, communication, and problem‑solving.

You will be proactive in learning and applying new knowledge setting the foundation for advancement in your MLOps career.

• Contributing to and building a shared ML production framework
• Managing cloud infrastructure on GCP using IaC
• Collaborating with teammates through technical discussions and code reviews
• Leading the MLOps aspects of AI solutions with full responsibility for building, deploying and maintaining solutions.
• Working with new product data scientists to move from POC through to production grade services
• Researching and experimenting with new MLOps tools or LLM integrations.
• Working with technical and non‑technical stakeholder groups to support feature or platform capability
• Assist in designing and implementing CI/CD pipelines for machine learning projects.
• Support the deployment and monitoring of machine learning models on cloud platforms.
• Help manage containerization and orchestration using Docker.
• Participate in implementing monitoring and logging solutions for model performance.
• Write and maintain clean, modular, and well‑documented code.
• Participate in code reviews and testing to ensure code quality.