MLOps Engineer

Job Role:

MLOps Engineer

Job Location:

Singapore

Experience:

8+ Years

Roles & Responsibilities

Design, build, and maintain scalable MLOps pipelines to support machine learning model development, deployment, and monitoring.

Develop and manage data pipelines and infrastructure for data science, analytics, and AI/ML workloads.

Collaborate with data scientists and engineering teams to validate use cases, test hypotheses, and operationalise machine learning solutions.

Automate model training, deployment, versioning, monitoring, and lifecycle management using MLOps best practices.

Ensure data pipelines and platform architecture support scalable, reliable, and reproducible ML workflows.

Monitor ML models and data pipelines to optimise performance, reliability, and operational efficiency.

Implement CI/CD practices, automation, and infrastructure-as-code for ML environments.

Prepare technical documentation and support knowledge transfer to internal teams.

Skills & Requirements

Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related discipline.

Minimum 8 years of experience in MLOps, Machine Learning Engineering, Data Engineering, or Data Science.

Proven experience building, deploying, and maintaining machine learning pipelines in cloud environments.

Strong understanding of MLOps practices, model lifecycle management, CI/CD, and workflow automation.

Experience working with data scientists to develop, validate, and deploy machine learning solutions.

Familiarity with enterprise data platform architectures supporting AI/ML workloads.

Proficiency in Python, SQL, and cloud-native data and ML services is preferred.

Strong analytical, problem-solving, collaboration, and communication skills.

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