ML Engineer

About the Role

Space Executive is hiring an ML Engineer in Singapore to take machine learning models out of the lab and into dependable production environments. This role is very hands-on and sits where machine learning meets platform engineering. You will partner with data scientists to understand how models are used, then build the systems that make deployment, monitoring, retraining, and maintenance consistent and repeatable.

Key Responsibilities

• Take ownership of pushing ML models into live production systems and building the supporting infrastructure.
• Create and support ML pipelines with tools such as MLflow, Airflow, and similar platforms, including experiment tracking, version control for models, deployment, and automated retraining flows.
• Set up monitoring and drift detection so production models continue to perform reliably over time.
• Develop and manage cloud infrastructure across AWS, GCP, or Azure, using Docker, Kubernetes, and CI/CD practices.
• Work side by side with data scientists to understand their workflows and create platform tools that let them deploy and improve models with less dependency.
• Produce clean Python code and help raise engineering quality and standards within the team.

Requirements

• 2 to 4 years of practical experience in ML engineering or a closely related field.
• Proven experience taking ML models into production, beyond just training or experimentation.
• Hands-on exposure to ML lifecycle platforms such as MLflow, Kubeflow, SageMaker, Vertex AI, or equivalent tools.
• Experience implementing production model monitoring and drift detection.
• Comfort working with Docker, Kubernetes, and CI/CD pipelines.
• Experience with cloud platforms such as AWS, GCP, or Azure.
• Strong Python programming ability along with solid software engineering fundamentals.
• Experience with workflow orchestration tools such as Airflow, Prefect, or similar systems.

Preferred Background

• Exposure to supply chain AI use cases such as demand forecasting or inventory optimisation.
• Familiarity with classic machine learning approaches like time-series forecasting, gradient boosted trees, and regression models, rather than only GenAI or LLM-based work.
• Experience with infrastructure-as-code tools such as Terraform or Helm.

Additional Information

This is a full-time onsite role based in Singapore, Singapore. No salary, stipend, number of openings, start date, or application deadline were provided in the source.