MLOps Engineer

Epergne Solutions; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Epergne Solutions seeks an experienced MLOps Engineer to design and maintain scalable ML pipelines, data infrastructure, and deployment workflows in cloud environments. You will work with data scientists and engineers to validate use cases, automate training and deployment, and ensure robust ML lifecycle management.

Ideal candidates bring 8+ years in MLOps or related data roles, strong Python/SQL skills, and solid collaboration and communication abilities to drive AI/ML initiatives across teams

Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or related discipline.
• 8+ years of experience in MLOps, ML Engineering, Data Engineering, or Data Science.
• Proven experience building, deploying, and maintaining ML pipelines in cloud environments.
• Strong understanding of MLOps practices, model lifecycle management, CI/CD, and workflow automation.

Responsibilities

• Design, build, and maintain scalable MLOps pipelines to support ML 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 ML 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

MLOps

Machine Learning

Data Engineering

CI/CD

Python

SQL

Cloud

Collaboration

Education

Bachelor's degree

Job description

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.