Remote Senior MLOps Engineer: Production ML Pipelines

Constructor Tech DACH; Singapore; Hybrid; SGD 110,000 - 180,000; Full time

Benefits offered by this job

English classes

Flexible schedule

Newborn bonus

Patent remuneration

Paid leave

Remote work option

Hybrid work (2 days in-office, 3 days

Job summary

Constructor is seeking a seasoned MLOps/DevOps engineer to design, build, and maintain scalable ML infrastructure. You will implement CI/CD pipelines, model deployment and rollback workflows, and observability for production models.

You will collaborate with ML, backend, and platform teams, leverage MLflow for experiment tracking, and ensure robust, reproducible model lifecycles across cloud environments. This hybrid role offers flexible scheduling and growth opportunities.

Qualifications

• 5+ years of MLOps or DevOps engineering experience.
• Experience 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) and workflow orchestration (Kubeflow).

Responsibilities

• 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.

Skills

MLOps

Docker

Kubernetes

CI/CD

Cloud platforms (Azure/AWS/GCP)

MLflow

PyTorch

Workflow orchestration (Kubeflow)

Education

Bachelor's degree in Computer Science, Engineering, or related field

Tools

GitLab CI

Terraform

Job description

Constructor is seeking a seasoned MLOps/DevOps engineer to design, build, and maintain scalable ML infrastructure. You will implement CI/CD pipelines, model deployment and rollback workflows, and observability for production models.

You will collaborate with ML, backend, and platform teams, leverage MLflow for experiment tracking, and ensure robust, reproducible model lifecycles across cloud environments. This hybrid role offers flexible scheduling and growth opportunities.