Senior AI Systems Engineer / Architect

Overview CSG AI is building advanced AI systems for Keysight CSG that bring together agentic reasoning, machine learning, reinforcement learning, engineering software, simulation, emulation, measurement, and product integration to transform how complex engineering designs are explored, evaluated, and improved.

Our ambition is to move beyond isolated AI assistants and point solutions. We are building systems that can understand engineering intent, reason across tools and data, coordinate specialized capabilities, act on virtual and physical engineering environments, and learn from the results.

The goal is a foundation for AI-driven engineering at scale, where intelligent systems can connect design intent to simulation, emulation, instruments, measurements, and product workflows, helping engineers explore larger design spaces and turn complex workflows into repeatable, adaptive, and increasingly autonomous capabilities.

Responsibilities Role Overview

As a Senior AI Systems Engineer / Architect, you will define and build the architectural backbone that connects AI reasoning with Keysight engineering tools, data, models, services, and products. You will design how agents represent state and context, coordinate work, invoke tools, exchange structured information, and interact with engineering environments.

Your systems will span the full fidelity spectrum, from software simulators and digital models, through real-time and hardware-in-the-loop emulators, to instruments, measurement systems, lab automation, and physical test workflows. You will create the integration patterns that allow AI capabilities to operate coherently across these environments rather than as isolated prototypes.

This is a hands-on architecture and systems engineering role. You will shape core agentic abstractions while also building production-quality software, APIs, orchestration services, tool interfaces, and backend integrations. You will work closely with ML, RL, Agentic DevSecOps, product, and domain teams to turn advanced AI capabilities into engineering systems that are powerful, extensible, reliable, and ready to become part of Keysight products and workflows.

Key Responsibilities

• Architect and implement the core agentic systems that connect LLMs and other reasoning models with engineering tools, APIs, data services, learned models, policies, and product workflows.
• Design orchestration and multi-agent reasoning flows using graph-based, tool-calling, or equivalent architectures, including clear semantics for state, context, memory, coordination, and execution.
• Build backend services, APIs, SDKs, and integration layers that allow intelligent systems to invoke engineering capabilities, retrieve context, execute multi-step workflows, and return structured, verifiable results.
• Integrate AI systems with Keysight CSG engineering environments across design, simulation, emulation, physical measurement, test automation, laboratory systems, and product software.
• Define robust agent and tool contracts, structured schemas, validation interfaces, state models, and error handling patterns that support predictable and extensible execution.
• Design context, retrieval, memory, and persistence mechanisms that allow AI systems to reason over engineering intent, product knowledge, design artifacts, simulation and emulation results, measurement data, and workflow history.
• Build production-quality orchestration services with modular architecture, versioning, observability, tracing, auditability, and clear failure and recovery behavior.
• Create reusable integration patterns for connecting AI systems with existing engineering platforms, enterprise services, data pipelines, compute environments, and product software.
• Collaborate with product, UX, and application teams to turn agentic capabilities into compelling engineering experiences through product features, operator workflows, APIs, and intelligent assistants.
• Partner with Agentic DevSecOps and runtime teams so agent workflows can be validated, monitored, governed, replayed, and operated reliably in production environments.
• Work with ML and RL engineers to integrate learned models, representations, policies, evaluations, and feedback loops into larger agentic systems.
• Prototype new agentic concepts rapidly, then harden the successful ones into maintainable, testable, documented, and production-quality software components.
• Define tec…