AI-ML Ops Engineer

ST Engineering is a global technology, defence and engineering group with offices across Asia, Europe, the Middle East and the U.S., serving customers in more than 100 countries. The Group uses technology and innovation to solve real-world problems and improve lives through its diverse portfolio of businesses across the aerospace, smart city, defence and public security segments. Headquartered in Singapore, ST Engineering ranks among the largest companies listed on the Singapore Exchange.

Join our Cyber Team

We are an industry leader in cybersecurity with over two decades of experience, we deliver a holistic suite of trusted cybersecurity solutions to empower cyber resilience for government and ministries, critical infrastructure, and commercial enterprises. Backed by our indigenous capabilities and deep domain expertise, we offer robust cyber-secure products and services in cryptography, cybersecurity engineering, digital authentication, SCADA protection, audit and compliance. We specialise in the design and build of security operations centres for cybersecurity professionals and provide managed security services to strengthen the cybersecurity posture of our government and enterprise customers.

The incumbent will automate and manage machine learning pipelines, enabling seamless model retraining, testing, and deployment to ensure reliable and efficient AI operations. 

This role is ideal for a hands-on MLOps Engineer who thrives on automating complex AI workflows and ensuring the seamless, reliable operation of machine learning systems in production 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 

• 3–6 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

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ST Engineering believes in fostering a culture where team members are encouraged to overcome challenges, explore new ideas, and work together to succeed. We value individuals who are determined to push beyond the boundaries, and have a thirst for knowledge, continuous learning, and self-improvement.