Machine Learning Engineer (AL-FNC250910 005/01)
At a glance
Mid-level Machine Learning Engineering role at Xcellink Pte Ltd. Singapore · contract.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and maintain scalable machine learning infrastructure with Kubeflow, MLflow and RunAI
- Automate ML environment setup and pipeline operations so they run consistently and reliably
- Test and validate models before release, then track their performance and health using monitoring tools
- Use performance measures to guide model retraining and updates
- Turn requirements from data scientists, engineers and stakeholders into scalable solutions, and take part in agile ceremonies
What you bring
- You can work with Linux and Python, plus JavaScript or another scripting language
- You have experience with Docker, Kubernetes and ML infrastructure tools such as Kubeflow or MLflow
- You understand the ML lifecycle from model development and training through validation and deployment
Who this fits
This contractor role suits a mid-level ML engineer who wants hands-on responsibility for infrastructure, pipelines and model operations. You’ll work onsite in Singapore and collaborate with data scientists, engineers and stakeholders. The posting prefers at least two years of relevant experience.
From the employer
We are looking for an ML Engineer to develop and scale our AI platform, build robust ML pipelines, and enable the deployment of AI workloads.
What You Will Be Working On
- Design, build, and maintain scalable ML infrastructure using tools like Kubeflow, MLflow, and RunAI.
- Automate the setup and management of ML environments and pipelines to ensure consistency and reliability.
- Ensure automated testing and validation of AI/ML models before deployment.
- Monitor model performance and health, using tools for logging, monitoring, and alerting.
- Implement strategies for model retraining and updating based on performance metrics.
- Collaborate with data scientists, engineers, and stakeholders to translate requirements into scalable ML solutions.
- Participate actively in agile ceremonies such as sprint planning, daily stand-ups, retrospectives, and backlog grooming.
Requirements
Skilled in Linux and Python, with knowledge of JavaScript or other scripting languages.
Experience with containerized applications (Docker, Kubernetes) and ML infrastructure tools such as Kubeflow, MLflow, and RunAI.
Familiarity with end-to-end ML lifecycle management, including model development, training, validation, and deployment.
Strong problem-solving skills and ability to troubleshoot complex issues.
Excellent communication and collaboration skills.
Ability to work in a fast-paced, dynamic environment.
Continuous learning mindset to keep up with technological advancements.
Preferably minimum 2 years of relevant working experience.