AI R&D Engineer (SDLC Acceleration)

Keysight Technologies, Inc.•Santa Rosa, CA

4d•Remote

About The Position

Keysight is a technology innovation leader, providing solutions in electronic design, simulation, test, manufacturing, and optimization across various markets. With a global team of approximately 16,800 employees, Keysight delivers world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets. The company fosters an award-winning culture that encourages creativity, innovation, and belonging, enabling employees to thrive throughout their careers. This role focuses on designing, building, and integrating AI-powered applications and services to accelerate software delivery and enhance the developer experience. The engineer will transform problem statements into production-grade solutions, including LLM-assisted features, RAG/agent workflows, and AI-augmented test automation, ensuring the secure and compliant use of AI throughout the Software Development Life Cycle (SDLC). A key aspect of the role involves enabling agentic workflows by integrating commercial AI tooling into Keysight’s Software Engineering and R&D functions.

Requirements

Bachelor’s or master's in computer science, electrical/electronic engineering, or related field; advanced ML/AI coursework or certifications preferred.
4-8 years in Computer / Software engineering (backend/frontend) with hands-on AI/ML application development or integration; internships/projects count toward demonstrable capability.
Proficiency in Python and C#/JavaScript/TypeScript; strong API design (REST/GraphQL), microservices.
Practical experience with LLM tooling (prompting, evaluation, safety), RAG (embeddings/vector stores), and at least one framework (PyTorch, TensorFlow).
Experience on Azure (preferred) or AWS CI/CD, containers (Docker/K8s), observability (logs/metrics/traces).
Familiarity with Bitbucket, GitHub, Jira/Confluence, Artifactory, Microsoft 365; familiarity with GitHub Copilot and M365 Copilot for developer productivity.
Ability to implement secure least-privilege, data minimization, and secure prompt/data flows.
Clear communication, collaboration across global teams, and a builder’s mindset with ownership of outcomes.

Nice To Haves

Exposure to NIST SSDF practices and integrating AI features into standard Keysight SDLC.
Frontend skills (Angular/React) to deliver end-to-end AI-augmented workflows.
Experience contributing to enablement of guilds or creating internal playbooks/tutorials.

Responsibilities

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 Agentic workflows: Use commercial AI tooling for inducting Agentic AI into Keysight’s Software Engineering and R&D functions.
Partner 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 frontends.
Create “golden-path” reference components for common scenarios (code assistance, documentation summarization, knowledge search, test plan generation).
Demonstrate and produce agentic workflows for security scanning, remediation, and pipeline automation.
Integrate solutions for central tools (Jira, Bitbucket, Confluence, Docker, Kubernetes, Jenkins, VMware, CloudBees CI/CD etc.).
Collaborate with Secure Software Factory (SSF) and Product Security Applications teams to fold AI features into standard SDLC workflows.
Help set up telemetry endpoints and build dashboards/reports that gather adoption and utilization data and capture real-time value benefits of AI tools.
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.
Translate corporate AI governance into developer-friendly approaches: data handling, prompt safety, model access tiers, applying vendor usage rules.
Partner with guilds/forums (NPC, enablement sessions) to share best practices, reusable assets, and developer training content.
Coordinate on releases with IT to ensure new features are tested and enabled for Keysight’s Software Engineering community.