Manager - AI & Machine Learning

Manager AI & Machine Learning Engineering

This leadership role combines strategic planning, technical mentorship, and hands-on collaboration with business stakeholders and data teams. You will help define Keysight's AI/ML roadmap and build the engineering backbone that enables model delivery, integration, and value realization at scale.

Key Responsibilities:

1.

Team Leadership & Talent Development

• Lead, mentor, and grow a team of ML engineers with a focus on innovation, quality, and cross-functional collaboration.
• Provide architectural and technical oversight while setting clear goals and success metrics aligned with enterprise strategy.
• Foster a culture of learning, experimentation, and continuous improvement.

2.

Program Execution & Delivery

• Drive delivery of ML solutions for high-value business use cases including:

• Customer churn prediction

• Upsell opportunity scoring

• Demand forecasting

• Operational and fulfillment risk detection

• Oversee the full ML delivery lifecycle from problem definition to deployment and feedback integration.

3.

ML Engineering & MLOps Strategy

• Define and implement best practices for MLOps, including:

• Model CI/CD pipelines

• Model versioning and monitoring

• Feature store architecture

• Performance tracking

• Utilize tools such as

  MLflow

  ,

  Airflow

  ,

  SageMaker

  ,

  Azure ML

  , or

  Kubeflow

  to ensure scalable and automated deployments.

4.

Functional & Domain Alignment

• Work with business domain leaders to align ML initiatives with strategic goals across:

• Sales

  (lead scoring, renewal prioritization)

• Customer Service

  (case triage, sentiment analysis)

• Finance

  (forecasting, anomaly detection)

• Supply Chain

  (logistics optimization, inventory planning)

• Order Fulfillment

  (ETA predictions, risk flagging)
• Ensure end-to-end adoption and success measurement of deployed models.

5.

Technology & Architecture Oversight

• Collaborate with infrastructure and platform teams to leverage enterprise technologies like:

• Snowflake

  ,

  DataRobot

  ,

  dbt

  ,

  Matillion

• Cloud platforms

  : AWS, Azure, or GCP
• Ensure seamless model integration into systems such as

  Salesforce

  ,

  Oracle Fusion Cloud

  ,

  ServiceNow

  , and other enterprise tools.

Qualifications:

Required:

• 8+ years

  of experience in machine learning, data science, or AI engineering roles.

• 3+ years

  in a leadership or managerial capacity, overseeing technical teams.
• Proven track record in delivering ML models to production in enterprise environments.
• Proficiency in

  Python

  , ML frameworks (

  scikit-learn

  ,

  PyTorch

  ,

  TensorFlow

  ), and ML orchestration tools.
• Solid grasp of

  MLOps best practices

  , pipeline automation, and lifecycle management.
• Demonstrated ability to work cross-functionally with product, data, and business teams.