As a Machine Learning Ops Engineer, you will specialise in the operational side of machine learning by automating model training, deployment, monitoring, and lifecycle management. You will help ensure that production AI models remain reliable, reproducible, and continuously improving across enterprise environments.
What You’ll Do and How You’ll Succeed
- Build and maintain automated ML training pipelines, including scheduled retraining and hyperparameter tuning.
- Implement model versioning, experiment tracking, and model registry practices using tools such as MLflow and Weights & Biases.
- Design model monitoring dashboards to track performance drift, data drift, and prediction distribution shifts.
- Set up A/B testing infrastructure to compare model performance in production.
- Manage GPU and compute resources for model training, including cost optimisation and scheduling.
- Implement automated rollback procedures for model deployments.
- Create model documentation and audit trails to support governance compliance.
We’d Love to Hear From You If…
Experience
- You have 3+ years of experience in DevOps or SRE, with at least 2 years focused on ML systems.
Technical Expertise
- You have experience with CI/CD tools such as Azure DevOps, GitHub Actions, or Jenkins.
- You are proficient in infrastructure-as-code tools such as Terraform, ARM templates, or CloudFormation.
- You are familiar with ML platforms such as Databricks, SageMaker, Vertex AI, or MLflow.
- You bring strong Linux, networking, and containerisation skills.
- You have experience with monitoring tools such as Grafana, Prometheus, or Datadog.
Ways of Working
- You take a disciplined approach to operational reliability, repeatability, and lifecycle management for ML systems.
- You are comfortable balancing model performance, governance, cost optimisation, and production stability.
Assignment Details
- Location: McKinley, Taguig City
- Work Set-Up: Hybrid