Expert R&D Software Engineer – AI / ML

Keysight is at the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~16,800 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn more about what we do.

Our award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers.

We are seeking an Expert R&D Software Engineer to lead the design and development of advanced AI-powered applications, driving end-to-end architecture across frontend, backend, and AI/ML systems. This role requires deep technical expertise to define system architecture, guide implementation, and ensure scalability, reliability, and performance of complex, production-grade solutions.

You will lead the integration of AI/ML capabilities, including large language models (LLMs) and advanced inference systems, into enterprise applications, while architecting robust data pipelines for large-scale data processing and AI-driven features. In addition to hands-on development, you will play a key role in shaping technical direction, establishing best practices, and mentoring engineers, operating as a technical authority within a fast-paced R&D environment.

Key Responsibilities
• Lead architecture and design of end-to-end AI-powered full-stack systems, spanning frontend, backend, and data/ML layers
• Define and drive technical strategy for integrating AI/ML capabilities into scalable, production-grade applications
• Design complex, high-performance frontend systems and user experiences for AI-driven workflows
• Architect and oversee scalable backend services, APIs, and distributed systems
• Establish and optimize data pipelines for large-scale ingestion, processing, and AI model integration
• Ensure system-wide performance, scalability, reliability, and security across the full stack
• Provide technical leadership across teams, guiding design decisions, code quality, and engineering standards
• Mentor senior and junior engineers, fostering technical growth and best practices
• Collaborate with product, R&D, and leadership to translate business needs into technical solutions and roadmaps
• Drive rapid prototyping and experimentation while ensuring a path to production-quality systems
• Lead adoption of modern engineering practices (CI/CD, observability, MLOps, cloud-native architecture)
• Evaluate and introduce new technologies, frameworks, and AI approaches to advance product capabilities

• Master’s or PhD in Computer Science, Software Engineering, or a related field, with 8–12+ years of experience in software engineering, including significant full-stack development
• Proven track record of architecting and delivering large-scale, production-grade systems with full-stack ownership
• Deep expertise in frontend (React, Vue, or similar) and backend technologies (Python, Node.js, Java, or equivalent)
• Strong experience integrating AI/ML systems (e.g., LLMs, inference services) into real-world applications at scale
• Demonstrated success building scalable data pipelines, including summarization/information extraction and anomaly detection or predictive models on structured/time-series data
• Strong knowledge of system design, distributed systems, and software architecture principles
• Experience with cloud platforms (AWS) and cloud-native architectures
• Strong leadership, mentorship, and cross-functional collaboration skills
• Experience working in R&D or highly ambiguous environments, with the ability to drive from concept to production
• Fluency in English, including technical communication

Strongly Preferred
• Experience leading technical architecture across multiple teams or large-scale systems
• Hands-on experience with generative AI, LLM orchestration, and agent-based systems
• Experience with advanced frontend architecture for complex, data-rich or AI-driven applications
• Expertise in containerization and orchestration (Docker, Kubernetes) and scalable distributed systems
• Experience with streaming systems, real-time data pipelines, and event-driven architecture
• Familiarity with MLOps practices, model monitoring, and evaluation frameworks
• Experience with Model Context Protocol (MCP) or similar frameworks for standardized AI system integration
• Track record of driving innovation, introducing new technologies, or influencing engineering strategy at an organizational level

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