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 technical standards and architectural patterns for agent interfaces, tool integration, orchestration, APIs, context management, and product quality across CSG AI.
Qualifications
Required Qualifications
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, with the ability to move between high-level AI frameworks and lower-level engineering integrations.
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 clean, 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 clear 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.
Preferred Qualifications
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.
Santa Rosa, CA Pay Range: MIN $156,740.00 - MAX $261,230.00
Careers Privacy Statement***Keysight is an Equal Opportunity Employer.***
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 technical standards and architectural patterns for agent interfaces, tool integration, orchestration, APIs, context management, and product quality across CSG AI.
Required Qualifications
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, with the ability to move between high-level AI frameworks and lower-level engineering integrations.
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 clean, 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 clear 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.
Preferred Qualifications
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.
Santa Rosa, CA Pay Range: MIN $156,740.00 - MAX $261,230.00
Careers Privacy Statement***Keysight is an Equal Opportunity Employer.***
