Senior ML Ops Engineer — Scale Production Pipelines

Constructor Labs; Singapore; Hybride; SGD 120 000 - 180 000; Plein temps

Avantages offerts par ce poste

Remote work

Hybrid work (2 days in-office, 3 days

Newborn bonus (€500 per child)

Patent remuneration

Paid leave

English classes

Equipment provided

Résumé du poste

Constructor seeks a seasoned MLOps/DevOps engineer to design and maintain ML production infrastructure. You will build CI/CD pipelines, deploy and rollback ML models, and implement monitoring and observability standards across services.

Experience with Docker/Kubernetes, GitLab CI, Terraform, and major cloud platforms (Azure, AWS, GCP) is required. Collaboration with ML, backend, and platform teams is essential.

Qualifications

• 5+ years of MLOps or DevOps engineering experience.
• Strong with containers and orchestration (Docker, Kubernetes).
• Experience with CI/CD (GitLab CI) and infrastructure-as-code (Terraform or similar).
• Hands-on with a major cloud platform (Azure, AWS, or GCP).
• Hands-on experience with MLflow for experiment tracking and model registry.
• Familiarity with ML frameworks (PyTorch), workflow orchestration (Kubeflow or similar), and monitoring stacks.

Responsabilités

• Design and maintain CI/CD pipelines for ML models and services.
• Build and operate model deployment, serving, and rollback workflows.
• Implement monitoring, observability, and alerting for models in production.
• Manage the model lifecycle with MLflow: experiment tracking, versioning, registries, and reproducibility.
• Automate infrastructure and partner with ML and platform teams on standards.

Connaissances

MLOps

DevOps

Docker

Kubernetes

GitLab CI

Terraform

Cloud platforms

MLflow

PyTorch

Kubeflow

Monitoring

Formation

Bachelor's degree or higher in CS/Engineering

Outils

Docker

Kubernetes

GitLab CI

Terraform

MLflow

Description du poste

Constructor seeks a seasoned MLOps/DevOps engineer to design and maintain ML production infrastructure. You will build CI/CD pipelines, deploy and rollback ML models, and implement monitoring and observability standards across services.

Experience with Docker/Kubernetes, GitLab CI, Terraform, and major cloud platforms (Azure, AWS, GCP) is required. Collaboration with ML, backend, and platform teams is essential.