Senior AI Systems Engineer / Architect

Keysight Technologies, Inc.•Santa Rosa, CA

About The Position

CSG AI is building advanced AI systems for Keysight CSG that combine agentic reasoning, machine learning, reinforcement learning, engineering software, simulation, emulation, measurement, and product integration. The aim is to enable AI-driven engineering systems that understand engineering intent, reason across tools and data, coordinate specialized capabilities, interact with virtual and physical environments, and learn from results. The Senior AI Systems Engineer / Architect defines and builds the architecture connecting AI reasoning with Keysight engineering tools, data, models, services, and products. The role spans software simulators and digital models through real-time and hardware-in-the-loop emulators, instruments, measurement systems, lab automation, and physical test workflows. It combines hands-on architecture with production software development, including agentic abstractions, APIs, orchestration services, tool interfaces, and backend integrations. The engineer collaborates with ML, RL, Agentic DevSecOps, product, and domain teams to deliver extensible, reliable AI capabilities suitable for Keysight products and workflows.

Requirements

BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field, or equivalent industry experience.
Typically 5+ years of relevant experience building production software systems, AI platforms, distributed applications, engineering tools, or intelligent systems, with demonstrated ownership of system architecture or complex integrations.
Strong software engineering skills in Python and C++ or another production systems language.
Experience architecting and building backend services, REST or gRPC APIs, SDKs, service integrations, and production software components.
Hands-on experience with LLM application development, tool-calling, agentic workflows, RAG, context management, or orchestration frameworks such as LangGraph, LangChain, or equivalent systems.
Strong understanding of distributed systems, asynchronous execution, state management, concurrency, reliability patterns, and integration architecture.
Experience integrating software systems with external engineering tools, data sources, compute systems, workflow engines, or enterprise platforms.
Ability to design structured interfaces for agents, tools, data exchange, validation, authorization, and error recovery.
Experience building maintainable production software with automated testing, version control, CI/CD practices, observability, profiling, and technical documentation.
Ability to translate emerging AI techniques into practical system architectures and collaborate across AI research, systems engineering, product engineering, and technical domain teams.

Nice To Haves

Experience integrating AI systems with engineering design, simulation, emulation, measurement, test automation, lab instrumentation, or other physical engineering workflows.
Experience with Keysight or similar electronic design, validation, measurement, test, lab automation, wireless, semiconductor, or telecom engineering environments.
Experience with Model Context Protocol (MCP), Agent-to-Agent communication, structured tool protocols, or custom agent runtime interfaces and interoperability standards.
Experience with vector databases, knowledge graphs, semantic retrieval, multimodal context, document processing, or engineering knowledge systems.
Experience designing product-facing AI features, copilots, autonomous engineering workflows, operator experiences, or AI-enabled engineering applications.
Experience with GPU-backed inference, model serving, prompt and model versioning, evaluation pipelines, and performance optimization for AI workloads.
Experience with Kubernetes, Docker, cloud-native or on-premises distributed services, observability tools, and production deployment patterns.
Familiarity with hardware-in-the-loop systems, digital twins, emulator control, instrument control, SCPI, VISA, measurement automation, or real-time engineering systems.
Experience with security, authorization, policy enforcement, audit trails, validation, or governance mechanisms for AI-enabled workflows.
Background in high-performance computing, scientific computing, EDA, wireless systems, semiconductor workflows, electronic measurement, or other complex engineering domains.

Responsibilities

Architect and implement agentic systems connecting LLMs and other reasoning models with engineering tools, APIs, data services, learned models, policies, and product workflows.
Design orchestration and multi-agent reasoning flows, including semantics for state, context, memory, coordination, and execution.
Build backend services, APIs, SDKs, and integration layers for invoking engineering capabilities, retrieving context, executing workflows, and returning structured, verifiable results.
Integrate AI systems with engineering environments for design, simulation, emulation, physical measurement, test automation, laboratory systems, and product software.
Define agent and tool contracts, schemas, validation interfaces, state models, and error-handling patterns.
Design context, retrieval, memory, and persistence mechanisms for 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 failure and recovery behavior.
Create reusable integration patterns for engineering platforms, enterprise services, data pipelines, compute environments, and product software.
Collaborate with product, UX, and application teams to develop agentic product features, operator workflows, APIs, and intelligent assistants.
Partner with Agentic DevSecOps and runtime teams to support validation, monitoring, governance, replay, and reliable production operation of agent workflows.
Work with ML and RL engineers to integrate learned models, representations, policies, evaluations, and feedback loops into agentic systems.
Prototype agentic concepts and harden successful ones into maintainable, testable, documented, production-quality components.
Define technical standards and architectural patterns for agent interfaces, tool integration, orchestration, APIs, context management, and product quality across CSG AI.