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 in their careers. We are seeking a Senior 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 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. Responsibilities 1. Machine Learning Development Deployment 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. 2. Cross-Functional ML Use Cases 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. 3. Model Governance and MLOps Ensure robust model monitoring, versioning, and retraining strategies to keep models reliable in dynamic environments. Work closely with DevOps and Data Engineering teams to automate deployment, CI/CD workflows, and cloud-native ML infrastructure (AWS/GCP/Azure). 4. Data Engineering and Feature Architecture 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 domain s business logic. 5. Communication Stakeholder Collaboration Present technical insights and model results to business and executive stakeholders in a clear, actionable format. Work with Product Owners and Program Managers to scope, prioritize, and plan delivery of ML projects. Qualifications Required: 4+ years of experience in machine learning, data science, or AI engineering, with a strong software engineering foundation. Proficiency in Python, and libraries such as scikit-learn, XGBoost, PyTorch, TensorFlow, or similar. Experience deploying models into production using ML pipelines and orchestration frameworks. Strong understanding of data structures, SQL, and cloud platforms (e.g., AWS SageMaker, Azure ML, or GCP Vertex AI). Preferred: Experience supporting business functions such as Finance, Sales, or Operations with ML use cases. Familiarity with MLOps tools (MLflow, SageMaker Pipelines, Feature Store). Exposure to enterprise data platforms (e.g., Snowflake, Oracle Fusion, Salesforce). Background in statistics, forecasting, optimization, or recommendation systems. Careers Privacy Statement Keysight is an Equal Opportunity Employer.1. Machine Learning Development Deployment 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. 2. Cross-Functional ML Use Cases 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. 3. Model Governance and MLOps Ensure robust model monitoring, versioning, and retraining strategies to keep models reliable in dynamic environments. Work closely with DevOps and Data Engineering teams to automate deployment, CI/CD workflows, and cloud-native ML infrastructure (AWS/GCP/Azure). 4. Data Engineering and Feature Architecture 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 domain s business logic. 5. Communication Stakeholder Collaboration Present technical insights and model results to business and executive stakeholders in a clear, actionable format. Work with Product Owners and Program Managers to scope, prioritize, and plan delivery of ML projects.