Machine Learning Engineer

Overview

Keysight is on 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 inლობtheir careers.

We are seeking a Machine Learning Engineer to lead the design, development, and deployment of scalable machine learning models that power business decisions across the enterprise. This role combines technical depth in ML/AI with a strong understanding

Administrator of business domains such as Sales, Service, Finance, Order Fulfillment, and Supply Chain. You will collaborate closely with Data Scientists, Data Engineers, and business partners to build production‑ready solutions that drive measurable impact.

• Design and implement supervised and unsupervised models for predictive analytics, including churn prediction, demand forecasting, renewal risk scoring, and cross‑sell/upsell opportunity identification.
• Translate business problems into ML frameworks and production solutions that improve efficiency, revenue, or customer experience.
• Build, optimize, and maintain ML pipelines using tools such as MLflow, Airflow, or Kubeflow.

• Partner with teams across Sales (e.g., lead scoring, next‑best action), Customer Service (e.g., case deflection, sentiment analysis), Finance (e.g., revenue forecasting, fraud detection), Supply Chain (e.g., inventory optimization, ETA prediction), and Order Fulfillment (e.g., delivery risk modeling) to define impactful ML use cases.
• Develop domain‑specific models and continuously improve them using feedback loops and real‑world performance data.

• Ensure robust model monitoring, versioning, and retraining strategies to keep models reliable in dynamic environments.
• Work closely with Dev Ops and Data Engineering teams to automate deployment, CI/CD workflows, and cloud‑native ML infrastructure (AWS/GCP/Azure).

• Collaborate with data engineers to define feature stores, data quality checks, and model‑ready datasets on platforms like Snowflake or Databricks.
• Perform feature selection, transformation, and engineering aligned with each domainics logic.
kunstenaars>