Job
Description
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 powerful, award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. Diversity, equity & inclusion are integral parts of our culture and drivers of innovation at Keysight. 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 looking for a Manager - AI & Machine Learning Engineering to lead a team of ML engineers in developing and deploying high-impact machine learning solutions across core enterprise functions including Sales, Service, Finance, Order Fulfillment, and Supply Chain. This role requires a strong mix of technical leadership, people management, and strategic alignment to guide ML engineers from ideation through production deployment. You ll play a key role in shaping the AI/ML delivery roadmap, establishing scalable engineering practices, and driving value through predictive models integrated into critical business workflows. Responsibilities 1. Team Leadership and Talent Development Manage, coach, and grow a high-performing team of machine learning engineers, promoting a culture of innovation, collaboration, and continuous learning. Provide technical direction, architectural oversight, and career mentorship. Define team objectives and success metrics aligned with enterprise priorities. 2. Program Execution and Delivery Drive the successful execution of ML use cases such as customer churn prediction, upsell opportunity scoring, demand forecasting, and operational risk detection. Work closely with data science, data engineering, product, and business stakeholders to define and deliver scalable ML solutions. Oversee delivery timelines, model development, deployment readiness, and feedback integration. 3. ML Engineering and MLOps Strategy Establish best practices in model development, deployment, and monitoring, using tools like MLflow, SageMaker, Azure ML, Airflow, or Kubeflow. Guide the team in implementing CI/CD for ML pipelines, model versioning, feature stores, and performance monitoring. Champion a strong foundation in software engineering, code quality, and reusability in ML development. 4. Functional & Cross-Domain Focus Align ML efforts with key business domains such as Sales (lead scoring, renewals), Service (case triage), Finance (forecasting), Order Fulfillment (ETA, risk), and Supply Chain (inventory planning, logistics optimization). Collaborate with business owners to prioritize high-impact ML use cases and ensure adoption and value realization. 5. Technology & Architecture Oversight Partner with data platform and infrastructure teams to scale ML solutions using Snowflake, Datarobots, and enterprise cloud platforms (AWS, Azure, GCP). Ensure ML models integrate seamlessly with business systems such as Salesforce, Oracle Fusion Cloud, and other operational tools. Qualifications Careers Privacy Statement ***Keysight is an Equal Opportunity Employer.**Required: 8+ years of experience in machine learning, data science, or engineering roles, with 3+ years in a technical leadership or management capacity. Proven experience building and deploying machine learning solutions in production environments. Hands-on background with Python, ML frameworks (scikit-learn, PyTorch, TensorFlow), and orchestration tools. Strong understanding of MLOps practices, model lifecycle management, and pipeline automation. Experience working with cross-functional stakeholders to deliver ML-powered business solutions. Preferred: Experience supporting business functions such as Sales, Finance, or Supply Chain with applied ML. Familiarity with cloud platforms (AWS, Azure, or GCP) and enterprise data tools (Snowflake, dbt, Matillion). Exposure to enterprise platforms such as Oracle Fusion Cloud, Salesforce, or ServiceNow. 1. Team Leadership and Talent Development Manage, coach, and grow a high-performing team of machine learning engineers, promoting a culture of innovation, collaboration, and continuous learning. Provide technical direction, architectural oversight, and career mentorship. Define team objectives and success metrics aligned with enterprise priorities. 2. Program Execution and Delivery Drive the successful execution of ML use cases such as customer churn prediction, upsell opportunity scoring, demand forecasting, and operational risk detection. Work closely with data science, data engineering, product, and business stakeholders to define and deliver scalable ML solutions. Oversee delivery timelines, model development, deployment readiness, and feedback integration. 3. ML Engineering and MLOps Strategy Establish best practices in model development, deployment, and monitoring, using tools like MLflow, SageMaker, Azure ML, Airflow, or Kubeflow. Guide the team in implementing CI/CD for ML pipelines, model versioning, feature stores, and performance monitoring. Champion a strong foundation in software engineering, code quality, and reusability in ML development. 4. Functional & Cross-Domain Focus Align ML efforts with key business domains such as Sales (lead scoring, renewals), Service (case triage), Finance (forecasting), Order Fulfillment (ETA, risk), and Supply Chain (inventory planning, logistics optimization). Collaborate with business owners to prioritize high-impact ML use cases and ensure adoption and value realization. 5. Technology & Architecture Oversight Partner with data platform and infrastructure teams to scale ML solutions using Snowflake, Datarobots, and enterprise cloud platforms (AWS, Azure, GCP). Ensure ML models integrate seamlessly with business systems such as Salesforce, Oracle Fusion Cloud, and other operational tools.
