Product Security Engineer — Agentic AI & Development

About Keysight:

Keysight is on the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~15,000 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 powerful, award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. Diversity, equity & inclusion are integral parts of our culture and drivers of innovation at Keysight. We believe that when people feel a sense of belonging, they can be more creative and innovative and thrive at all points in their careers.

About the Role

We are seeking a skilled and experienced product security engineer to design, develop, and integrate AI/ML‑enabled capabilities into Keysight’s security solutions as part of the Software Development Life Cycle (SDLC). This role is hands‑on and development‑focused, emphasizing software engineering excellence and applied AI/ML.

You will contribute to building intelligent features and automation that improve product security, quality, and efficiency in developer workflows. Familiarity with secure software practices is considered a plus. The primary focus is on software development, AI/ML integration, and learning at scale.

About the Team:

You will collaborate with product owners, security researchers, and full-stack engineers to build advanced autonomous security capabilities that enhance Keysight’s Software Development Life Cycle (SDLC).

Key Responsibilities:

Software Development

• Design, implement, test, and maintain backend services and tools using Python and related ecosystems.
• Build well‑structured, maintainable, and testable code following modern software engineering practices.
• Collaborate in design reviews, code reviews, and agile ceremonies to deliver high‑quality features.

Applied AI/ML

• Develop and integrate AI/ML models or services into production software systems.
• Work with structured and unstructured data to support training, validation, and inference pipelines.
• Integrate ML capabilities with APIs, services, and user interfaces.

Automation & SDLC Integration

• Contribute to automation that integrates with CI/CD pipelines and development workflows.
• Assist in building data ingestion, processing, and evaluation pipelines to support intelligent features.

Collaboration

• Work closely with software engineers, data practitioners, product owners, and quality engineers.
• Communicate technical designs and implementation details clearly across teams.

Must-Haves:

• Bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, AI/ML, or a related field.
• 3–5 years of hands‑on software development experience, with a preference for work involving AI/ML or data‑driven systems.
• Strong proficiency in Python with experience building and maintaining production‑grade software.
• Solid understanding of data structures, algorithms, and object‑oriented design.
• 1–3+ years of practical AI/ML exposure, such as:
• Developing, training, deploying, or integrating machine learning or agentic AI models
• Embedding AI/ML capabilities into production software systems
• Experience with Git, modern CI/CD pipelines, and standard development workflows.
• Familiarity with LLMs or AI‑assisted workflows is a plus.
• General awareness of secure software development practices is helpful but not required.
• Ability to work independently, contribute to technical decisions, and collaborate effectively across teams.
• Clear communication skills and proven ability to work in cross‑functional environments.

We value:

• Master’s degree in Computer Science, Computer Engineering, Software Engineering, or a related technical field.
• Strong interest in software craftsmanship and applied AI/ML.
• Curiosity and willingness to learn new tools, frameworks, and technologies.
• Practical problem‑solving skills and attention to detail.
• Ability to work cross‑functionally and contribute positively to team outcomes.