Senior Architect – Agentic Orchestration Frameworks

Senior Architect – Agentic Orchestration Frameworks

Join to apply for the Senior Architect – Agentic Orchestration Frameworks role at Keysight Technologies

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.

Keysight’s Applied AI Autonomy Initiative is developing a next‑generation agentic orchestration framework that enables AI agents to reason, adapt, and coordinate across complex engineering workflows. Built on Lang Graph and reinforcement‑inspired feedback mechanisms, this framework transforms prompts and design intents into executable orchestration strategies that evolve autonomously through iterative simulation and validation loops.

Our ambition is not merely to replicate human reasoning, but to push past human limits, enabling agentic systems to explore design spaces, optimize engineering workflows, and evolve orchestration strategies at a scale and speed no human could achieve.

The goal is to create the foundational runtime for adaptive, multi‑agent reasoning at scale, where AI systems not only execute tasks but collaborate, refine, and self‑improve across engineering domains.

• Role Overview – Build the model intelligence and feedback infrastructure that allows engineering models to generalize across varying design and measurement scenarios, learn from real and simulated data streams, provide explainable and traceable predictions, and continuously improve performance and robustness through data‑driven refinement.
• Core Responsibility Domains
  • Engineering Model Creation & Neural Conditioning – Design and train ML models that capture engineering behaviors and physics‑based relationships; develop predictive and surrogate models using experimental, simulation, and sensor data; create feature representations and conditioning schemas; implement model pipelines that adapt to new devices, topologies, or domains with minimal retraining; collaborate with domain engineers to align ML model design with real‑world measurement and calibration semantics.
  • Data Intelligence, Feedback & Augmentation – Build robust data systems that convert engineering data into model‑ready intelligence; develop data ingestion, transformation, and validation pipelines for structured, semi‑structured, and streaming data; implement feedback loops where new simulation and measurement results automatically trigger data updates and retraining; design augmentation and normalization strategies to enhance data diversity, reduce bias, and improve model stability; ensure traceable data versioning and reproducibility with detailed lineage and metadata tracking.
  • Explainable AI & Diagnostic Analytics – Make engineering models transparent, interpretable, and auditable; integrate XAI methods such as SHAP, LIME, attention visualization, or gradient attribution into model training and validation workflows; develop diagnostic analytics dashboards to interpret model performance, bias, drift, and physical consistency; create data and model introspection tools that allow engineers to inspect how features influence predictions; establish confidence scoring and anomaly detection frameworks for model validation and trust in production applications.
• Key Responsibilities
  • Expand machine learning models portfolio for engineering and simulation‑driven applications.
  • Improve and maintain data pipelines for model ingestion, feature extraction, and structured conditioning.
  • Implement explainability and performance diagnostics to keep models interpretable and auditable.
  • Collaborate with simulation, measurement, and data science teams to align ML architectures with engineering use cases.
  • Continuously refine and validate models using real‑world data feedback from measurement systems or simulation loops.

• A defining opportunity to build the machine learning foundation that powers Keysight’s next…