Manager - AI & Machine Learning
IN-KA-Bangalore
Global Careers (External)
Req #: 48308
Type: Regular
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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.
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