Senior AI Architect – Agentic Orchestration Frameworks

Overview:
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 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.

About the Team

Keysight’s Applied AI Autonomy Initiative is building next-generation AI systems that go beyond static models. The team develops agentic orchestration frameworks that allow AI agents to reason, adapt, and coordinate across complex engineering workflows involving simulation, measurement, and real-world data.

Working at the intersection of machine learning, scientific modeling, and engineering data, the team collaborates closely with simulation, measurement, and domain experts across Europe and globally.

About the Role

As a Senior Architect – Agentic Orchestration Frameworks, you will design the machine learning intelligence and data feedback foundations that enable engineering models to continuously learn, adapt, and explain their behavior.

This role is highly hands-on and architectural. You will shape how ML models are trained, conditioned, validated, and improved using both simulated and real measurement data, with a strong focus on explainability, traceability, and robustness.

The role sits at the crossroads of applied ML, data engineering, and scientific systems, with direct impact on how future engineering decisions are modeled and optimized.

Responsibilities:
Design and develop ML and hybrid models that capture engineering behavior and physics-based relationships.
Build data pipelines and feedback loops that continuously retrain and refine models using simulation and measurement results.
Define feature representations and conditioning strategies that encode physical parameters, constraints, and test configurations.
Integrate Explainable AI (XAI) techniques to ensure models are transparent, auditable, and trusted by engineers.
Develop diagnostics for model performance, drift, bias, and confidence scoring.
Collaborate closely with simulation, measurement, and domain engineering teams to align ML architectures with real engineering use cases.
Contribute to the overall architecture of adaptive, self-improving ML systems.

Qualifications:

Required Qualifications

PhD or 5+ years of hands-on experience in machine learning, applied data science, computational modeling, or similar fields.

Strong foundations in applied machine learning and data-driven modeling.

Experience developing ML models for engineering, physics-based, signal-processing, or scientific domains.

Solid programming skills in Python, with experience in data pipelines, feature engineering, and automation.

Practical experience with PyTorch (or similar frameworks).

Experience implementing model explainability or interpretability techniques.

Good understanding of data versioning, reproducibility, and model lifecycle management.

Desired Qualifications

Background in scientific computing, simulation-driven modeling, or surrogate models.

Experience with hybrid physical–statistical models.

Familiarity with uncertainty quantification, sensitivity analysis, or confidence estimation.

Exposure to HPC or GPU-based training environments.

Experience working with complex, multi-source engineering datasets.

Basic working knowledge of SQL and data schemas.

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