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 AI Solutions Architect, you will be the critical contributor for translating business challenges within the electronics manufacturing industry into robust, scalable, and innovative AI solutions. This individual will be a technical leader, bridging the gap between business needs, data science teams, and engineering teams to design, develop, and deploy AI solutions that drive strategic business value. They will possess deep expertise in AI/ML technologies, data architecture, and a strong understanding of the electronics manufacturing test.

• Collaborate with stakeholders (business, product, data science, engineering) to understand business challenges and define AI solution requirements.
• Design and architect end-to-end AI solutions, including data ingestion, data processing, model development, deployment, and monitoring.
• Develop detailed solution blueprints, technical specifications, and architecture diagrams.
• Research and assess AI/ML technologies particularly relevant to electronics manufacturing, including Computer Vision for defect detection, Time Series Analysis for predictive maintenance, and Natural Language
• Conduct proof-of-concepts (POCs) to validate the feasibility and effectiveness of proposed solutions including processing for analyzing maintenance logs.
• Define data requirements and design data pipelines for AI model training and inference, considering the high volume and velocity of data generated in an electronics manufacturing environment.
• Design and implement robust data governance and security practices, with a strong focus on data privacy and compliance.
• Collaborate on infrastructure design and optimization to support AI workloads – cloud or on-premises.
• Lead technical discussions and provide guidance to data science, engineering, and manufacturing operations teams.
• Mentor and coach data scientists and engineers on best practices for AI solution design and implementation in an electronics manufacturing context.
• Promote knowledge sharing and best practices within the organization.
• Define key performance indicators (KPIs) for AI solutions – focusing on metrics like defect reduction, equipment uptime, and yield improvement.
• Work with operations teams to monitor solution performance and identify opportunities for continuous optimization.
• Ensure solutions are scalable, reliable, and maintainable, considering the demanding requirements of a manufacturing environment.

• Bachelor’s degree in computer science, data science, engineering, or a related field. Master’s degree preferred.
• 8+ years of experience in software development, data engineering, or a related technical role.
• 5+ years of experience specifically working with AI/ML technologies (e.g., Deep Learning, Natural Language Processing, Computer Vision).
• Strong understanding of AI/ML algorithms, model training, and evaluation metrics.
• Experience with cloud platforms (e.g.AWS, Azure, Google Cloud) – specifically experience deploying and managing AI/ML solutions.
• Proficiency in programming languages such as C#, C++, Python, R, or Java.
• Experience with data warehousing, data lakes, and ETL processes.
• Excellent communication, collaboration, and problem-solving skills.
• Experience with DevOps practices (CI/CD, Infrastructure as Code).
• Experience with containerization technologies (Docker, Kubernetes).
• Familiarity with data governance frameworks and regulations (e.g., GDPR, CCPA).
• Experience with MLOps (Machine Learning Operations) practices.
• Certifications in relevant AI/ML technologies (e.g., AWS Certified Machine Learning Specialty, Azure AI Engineer Associate).

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