2 days ago
Base Salary
$157k - $261k/yr
Responsibilities
- Architect and implement agentic systems connecting LLMs and reasoning models with engineering tools, APIs, data services, learned models, policies, and product workflows.
- Design graph-based, tool-calling, or equivalent multi-agent orchestration with state, context, memory, coordination, and execution semantics.
- Build production backend services, REST or gRPC APIs, SDKs, integration layers, and structured tool interfaces.
- Integrate AI systems with design, simulation, emulation, physical measurement, test automation, laboratory, and product software environments.
- Define agent and tool contracts, schemas, validation interfaces, state models, authorization patterns, and error handling.
- Develop context, retrieval, memory, persistence, observability, tracing, auditability, versioning, and failure-recovery mechanisms.
- Create reusable integrations with engineering platforms, enterprise services, data pipelines, compute environments, workflow engines, and product software.
- Collaborate with ML, reinforcement learning, product, UX, application, domain, and Agentic DevSecOps teams.
- Prototype agentic concepts and harden successful prototypes into maintainable, testable, documented production software.
- Define technical standards and architectural patterns for agent interfaces, tool integration, orchestration, APIs, context management, and product quality.
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 ownership of system architecture or complex integrations.
- Strong software engineering skills in Python and C++ or another production systems language.
- Experience 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 or LangChain.
- Strong understanding of distributed systems, asynchronous execution, state management, concurrency, reliability patterns, and integration architecture.
- Experience integrating software 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 with automated testing, version control, CI/CD practices, observability, profiling, technical documentation, and maintainable production software.
- Preferred experience with engineering design, simulation, emulation, measurement, test automation, lab instrumentation, Keysight or similar environments, MCP, Agent-to-Agent communication, vector databases, knowledge graphs, semantic retrieval, multimodal context, GPU-backed inference, model serving, Kubernetes, Docker, hardware-in-the-loop systems, SCPI, VISA, security, authorization, governance, high-performance computing, scientific computing, EDA, wireless, semiconductor, or telecom systems.
Tech Stack
Categories
About Keysight
Keysight Technologies builds electronic test and measurement instruments and software used for design, validation, and manufacturing by engineers in communications, semiconductor, aerospace and defense, automotive, and data center markets. Its portfolio includes oscilloscopes, signal and network analyzers, RF/microwave test systems, and EDA and network test software, sold with services and support. Founded in 2014 as a spin-off from Agilent Technologies, Keysight is a public company (NYSE: KEYS) headquartered in Santa Rosa, California.