Technical jobs in Singapour

Keysight is at the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~15,000 employees create world‑class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn more about what we do.

Our award‑winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry‑first solutions. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers.

We are seeking a passionate and innovative AI architect to join our team in Electronic Industrial Solutions Group AI/ML Center of Excellence to launch AI solutions and models for future enterprise engagements. As a Senior Engineer, you will play a crucial role in the design, development, deployment, and maintenance of our AI/ML solutions. You’ll work collaboratively with data scientists, software engineers, and product managers to build and scale intelligent applications that solve complex business problems. You’ll be responsible for translating high‑level concepts into robust, production‑ready systems, ensuring the quality, scalability, and reliability of our AI/ML models and infrastructure. The ideal candidate will work closely with data scientists, engineers, and manufacturing operations teams in relevant AI/ML technologies.

You will work closely with strategic planners, product management, and customers to understand key challenges and create solutions that advance Keysight’s position as AI/ML leader in electronics measurement test.

• Design, develop, train, and evaluate AI/ML models for applications such as defect detection (Computer Vision), predictive maintenance, anomaly detection, and yield optimization - all within the context of electronics manufacturing.
• Implement robust model training pipelines, incorporating techniques like data augmentation, transfer learning, and hyperparameter tuning.ng, Communication, Collaboration, Leadership, Problem‑Solving
• Conduct rigorous model validation and testing, ensuring models meet performance requirements and are reliable in a production environment.
• Collaborate with data engineers to design and build scalable data pipelines for data ingestion, preprocessing, feature engineering, and model deployment.
• Work with time‑series data to build predictive models for equipment health and performance.
• Implement data quality monitoring and validation processes.
• Deploy AI/ML models into production environments, utilizing DevOps practices and MLOps workflows.
• Set up model monitoring systems to track performance, identify drift, and trigger retraining when necessary.
• Work with infrastructure teams to optimize model serving infrastructure.
• Collaborate closely with data scientists to translate business requirements into technical specifications.
• Mentor junior engineers and provide technical guidance on AI/ML best practices specifically for document models, pipelines, and deployment processes thoroughly.
• Stay abreast of the latest advancements in AI/ML, particularly within the context of industrial applications.
• Explore and evaluate new technologies and algorithms that could benefit our operations.

• Bachelor’s degree in computer science, Electrical Engineering, Industrial Engineering, or a related field. Master’s degree preferred.
• 2-4 years of experience in developing and deploying AI/ML models, with a strong focus on industrial applications (ideally in manufacturing test is a critical role responsible for translating business challenges within the
• Proficient in Python and experience with deep learning frameworks (e.g.TensorFlow, PyTorch, SageMaker).
• Strong understanding of statistical modeling, machine learning algorithms, and model evaluation techniques.
• Experience with data warehousing, data lakes, ETL processes.
• Familiarity with cloud computing platforms and DevOps practices, data architecture, and experience with version control systems.

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