ST Engineering is a global technology, defenceand engineering group with offices across Asia, Europe, the Middle East and theU.S., serving customers in more than 100 countries. The Group uses technologyand innovation to solve real-world problems and improve lives through itsdiverse portfolio of businesses across the aerospace, smart city, defence andpublic security segments. Headquartered in Singapore,ST Engineering ranks among the largest companies listed on the SingaporeExchange.
Join our Cyber Team
We are an industry leader in cybersecurity with over two decadesof experience, we deliver a holistic suite of trusted cybersecurity solutionsto empower cyber resilience for government and ministries, criticalinfrastructure, and commercial enterprises. Backed by our indigenouscapabilities and deep domain expertise, we offer robust cyber-secure productsand services in cryptography, cybersecurity engineering, digitalauthentication, SCADA protection, audit and compliance. We specialise in thedesign and build of security operations centres for cybersecurity professionalsand provide managed security services to strengthen the cybersecurity postureof our government and enterprise customers.
Theincumbent will automate and manage machine learning pipelines, enablingseamless model retraining, testing, and deployment to ensure reliable andefficient AI operations.
Thisrole is ideal for a hands-on MLOpsEngineer who thrives on automating complex AI workflows andensuring the seamless, reliable operation of machine learning systems inproduction environments.
Responsibilities
- Design, implement, and maintain automated machine learning pipelines for training, validation, testing, and deployment of AI models.
- Ensure continuous integration and delivery (CI/CD) of models into production environments, with support for versioning, rollback, and monitoring.
- Automate model retraining workflows based on triggers such as performance degradation, new data availability, or updated business requirements.
- Develop and manage infrastructure for scalable and reproducible ML experiments, using tools such as MLflow, Kubeflow, or similar.
- Collaborate with data scientists and AI engineers to ensure smooth handoff from experimentation to production.
- Monitor pipeline health, resource usage, and model performance in production, ensuring uptime and fast recovery from failures.
- Implement testing strategies for models, including unit tests, integration tests, and data validation checks.
- Optimize pipeline efficiency across compute, storage, and deployment layers.
Requirements
Experience
- Preferably possess 1-3 years of experience in MLOps, DevOps, or machine learning engineering with a focus on operationalizing AI workflows.
- Proven experience deploying and managing ML models in production environments.
Technical Skills
- Proficiency in Python and ML engineering tools (e.g., MLflow, Airflow, DVC, Kubeflow, or similar).
- Experience with containerization (Docker), orchestration (Kubernetes), and cloud services (AWS, GCP, Azure).
- Familiarity with CI/CD practices for ML and version control systems (e.g., Git).
- Understanding of monitoring, logging, and alerting for ML pipelines and models.
Preferred Knowledge
- Experience with agentic AI systems or workflows involving continuous learning and tool interaction.
- Knowledge of data drift detection, model validation, and feedback loop design.
- Exposure to real-time or streaming data pipelines (Kafka, Flink, etc.).
Work location: Jurong East
Findout more: https://www.stengg.com/cybersecurity
ST Engineering believes in fostering a culture where team membersare encouraged to overcome challenges, explore new ideas, and work together tosucceed. We value individuals who are determined to push beyond the boundaries,and have a thirst for knowledge, continuous learning, and self-improvement.
