Machine Learning Engineer

Company description:

Singapore Technologies Engineering Ltd

Job description:

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 design, build, and deploy agentic AI models, ensuring robust performance across digital twin simulations and real-world production systems.

This role calls for a hands-on Machine Learning Engineer with a deep understanding of both intelligent agent behavior and ML infrastructure, capable of bridging experimental models with production-grade AI solutions.

The incumbent will be a technically excellent and research-savvy AI Data Scientist who can push the boundaries of intelligent behavior—turning complex data and models into real-world, decision-capable AI systems.

Responsibilities

• Design and implement agentic AI models capable of autonomous decision-making, planning, and tool use in complex environments.
• Develop and train machine learning models tailored for both simulation-based (digital twin) testing and live production deployment.
• Translate high-level behavioral specifications into scalable, modular ML architectures that enable adaptive and interactive agents.
• Collaborate with data scientists, AI architects, and systems engineers to integrate models into broader AI pipelines and system architectures.
• Optimize models for efficiency, robustness, and real-time performance within distributed and resource-constrained environments.
• Implement continuous training, fine-tuning, and model evaluation workflows to support evolving agent behavior and dynamic environments.
• Ensure alignment of model outputs with system goals, safety constraints, and operational performance metrics.
• Contribute to MLOps pipelines, including model versioning, deployment automation, monitoring, and rollback mechanisms.

Requirements

Experience

• 5+ years of experience in machine learning engineering or applied AI, including model deployment in production environments.
• Demonstrated success in developing and deploying ML models within simulation and real-time operational contexts.

Technical Skills

• Proficiency in ML frameworks such as PyTorch, TensorFlow, and tools for agentic and reinforcement learning (e.g., Ray RLlib, Stable-Baselines).
• Strong experience with model training, evaluation, and deployment across both digital twins and live systems.
• Familiarity with MLOps, CI/CD pipelines for ML, and monitoring tools for model performance and drift detection.
• Understanding of agent-based modeling, planning algorithms, and state/action representations.
• Experience with containerization (Docker), orchestration (Kubernetes), and cloud platforms (AWS, GCP, or Azure) for ML workloads.

Preferred Knowledge

• Exposure to agentic AI systems, multi-agent coordination, or intelligent automation frameworks.
• Familiarity with digital twin architectures, synthetic data generation, and simulation-to-reality transfer techniques.
• Experience integrating models into service-oriented or microservice architectures.

Work location: Jurong East

Find out more:

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.