Keysight is at 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 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.

Keysight’s products address a variety of Test and Measurement requirements for 6G, Commercial Communications, EDA, Automotive, Semi-Conductor Test, Energy and Aero/Defense markets. Within Keysight - the Secure Software Factory (SSF) and R&D productivity initiative aim to integrate Artificial Intelligence tools to accelerate software development lifecycle (SDLC) along with adopting the enterprise DevSecOps platform.

We have an opportunity for AI R&D Engineer, to

• Design, build, and integrate AI‑powered applications and services that accelerate software delivery and developer experience.

• Turn problem statements into production‑grade solutions (LLM‑assisted features, RAG/agent workflows, AI‑augmented test automation) - ensure secure, compliant use of AI across the SDLC

• Enable integration of commercial AI tooling and SaaS solutions for inducting Agentic AI into Keysight’s Software Engineering and R&D functions

  Specific Responsibilities:

  AI Application Development

  • Partnering with IT to drive delivery of new features to internal functions to accelerate SDLC phases.
  • Combine classical ML, retrieval/agent patterns and leverage LLM capabilities to deliver robust APIs, microservices, and front‑ends.
  • Create “golden‑path” reference components for common scenarios (code assistance, documentation summarization, knowledge search, test plan generation).
  • Demonstrate and productionize agentic workflows for security scanning, remediation and pipeline automation

  Platform & Integration

  • Integrate solutions for central tools (Atlassian’s Jira, Bitbucket, confluence, Docker, Kubernetes, Jenkins, VMware, Cloud Bees CI/CD etal.)
  • Collaborate with Secure Software Factory (SSF) and Product Security Applications teams to fold AI features into standard SDLC workflows.

  Engineering Intelligence & Telemetry (SEI)

  • Help setup telemetry end points and Build dashboards/reports that gather adoption, utilization and capture real time value benefits of AI models and toolings.

  Quality, Test & Reliability

  • Design test strategies for AI features (unit, integration, adversarial, model‑in‑the‑loop); apply Human and AI‑assisted testing and demonstrate self‑healing approaches where suitable.

  Security, Privacy & Compliance

  • Translate corporate AI governance into developer friendly approaches: data handling, prompt safety, model access tiers, applying vendor usage rules.

  Collaboration & Enablement

  • Partner with guilds/forums (NPC, enablement sessions) to share best practices, reusable assets, and developer training content.
  • Participate in coordination with IT for releases to ensure new feature releases are tested and enabled for Software Engineers at large.

Education

• Bachelor’s or master's in computer science, Electrical/Electronic Engineering, or related field; advanced ML/AI coursework or certifications preferred.

Experience:

• 2–4 years in Computer / Software engineering (backend/frontend) with hands‑on AI/ML application development or integration; internships/projects count toward demonstrable capability

• Programming: Proficiency in Python and C#/JavaScript/TypeScript; strong API design (REST/GraphQL), microservices.

• AI/ML: Practical experience with LLM tooling (prompting, evaluation, safety), RAG (embeddings/vector stores), and at least one framework (PyTorch, TensorFlow).

• Cloud & DevOps: Experience on Azure (preferred) or AWS CI/CD, containers (Docker/K8s), observability (logs/metrics/traces).

• Toolchain: GitHub, Jira/Confluence, Microsoft 365; familiarity with GitHub Copilot and M365 Copilot for developer productivity.
• Security & Compliance: Ability to implement secure least‑privilege, data minimization, and secure prompt/data flows.
• Soft skills: Clear communication, collaboration across global teams, and a builder’s mindset with ownership of outcomes.

Preferred

• Exposure to NIST SSDF practices (https://csrc.nist.gov/projects/ssdf#ssdf-practices) and integrating AI features into standard Keysight SDLC

• Front‑end skills (Angular/React) to deliver end‑to‑end AI‑augmented workflows.

• Experience contributing to enablement of guilds or creating internal playbooks/tutorials.

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