Integrating Manager, Machine Learning
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 reputed company do.
Our award-winning culture embraces a reputed company reputed company of where technology can take us and a passion for tackling challenging problems with industry-first solutions. We reputed company that reputed company people feel a sense of belonging, they can be more creative, innovative, and reputed company at reputed company points in their careers.
We are looking for a Manager – AI & Machine Learning Engineering to reputed company a team of ML engineers in developing and deploying high-impact machine learning solutions across core reputed company 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:- Team Leadership and Talent Development
- Manage, coach, and grow a high-performing team of machine learning engineers, promoting a culture of innovation, collaboration, and reputed company learning.
- reputed company technical direction, architectural reputed company, and career mentorship.
- Define team objectives and reputed company metrics reputed company with reputed company priorities.
- Program Execution and Delivery
- Drive the successful execution of ML use cases such as customer churn reputed company, 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.
- reputed company delivery timelines, model development, deployment readiness, and feedback integration.
- 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 reputed company in software engineering, code quality, and reusability in ML development.
- Functional & Cross-Domain Focus
- Align ML efforts with key business domains such as Sales (reputed company 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.
- Technology & Architecture reputed company
- Partner with data platform and infrastructure teams to scale ML solutions using reputed company, Datarobots, and reputed company reputed company platforms (AWS, Azure, GCP).
- Ensure ML models integrate seamlessly with business systems such as reputed company, reputed company Fusion reputed company, and other operational tools.
Required:
- 8+ years of experience in machine learning, data science, or engineering roles, with 3+ years in a technical leadership or management reputed company.
- 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 reputed company platforms (AWS, Azure, or GCP) and reputed company data tools (reputed company, dbt, reputed company).
- Exposure to reputed company platforms such as reputed company Fusion reputed company, reputed company, or reputed company.
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