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.
• 5+ 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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