Principal AI Engineer; Software & AI Labs

Overview

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.

Software and AI Labs (SAL) at Keysight has an opportunity for an experienced engineering individual to join our team.

Candidate will be responsible for the following:

• Guiding safe adoption of AI Tools and applications.
• Benchmarking commercial models and industry patterns for software engineering and guiding SAL to make the right decisions for Keysight's engineering teams.
• Developing Agentic/RAG solutions for developer workflows and Dev Sec Ops  Processes with an objective to radically evolve Keysight's SDLC.
• Using data‑driven indicators from Software Engineering Intelligence (SEI) platform to help SAL codify best practices and quantify AI's impacts on Keysight's development velocity.
• Driving enablement via enterprise forums/guides and playbooks to scale AI across teams.
• Ensuring policy aligned, secure, and compliant AI usage across SDLC (e.g., ISO/IEC 42001, NIST AI RMF, SOC2, OWASP API Security).
• You will be a part of an agile and innovative team that believes in experimentation and championing of safe AI use for tackling software development process bottlenecks.

AI‑led Productivity & SDLC Acceleration

• Lead technical evaluations and rollouts of AI tools for the enterprise and work with partners to define adoption of roadmaps and guardrails.
• Design high‑impact solution patterns (prompt libraries, RAG architectures, agent workflows) for planning, coding, testing, documentation – with a goal to improve productivity.
• Build reference implementations and "golden paths" that integrate AI with SSF tool chains.
• Evaluate upcoming AI technology concepts (MCP, Agentic AI) and guide Software Engineering teams to safe adoption.

Architecture & Integration

• Architect end-to-end AI solutions that bridge or span on‑prem, cloud, or VPC resources; optimize solutions for faster cadence, lower cost, and greater developer experience.
• Partner with Central Engineering team to embed AI into planning, testing, documentation, CI/CD, and release processes.

Governance, Risk & Compliance

• Translate corporate AI governance into developer‑friendly approaches: data handling, prompt safety, model access tiers, and vendor usage rules.
• Align practices broadly to concepts as defined in ISO/IEC 42001 (AI management systems), NIST AI RMF, SOC 2 controls, and OWASP API Security within SAL's Secure Software Factory (SSF) context.

Stakeholder, Vendor & Program Management

Evaluate and manage vendor relationships, set up pilot partnering with businesses and IT, benchmarking, and compliance obligations.

• Engage with engineering leaders, IT, and adjacent business units to coordinate rollouts and validation sessions.
• Provide concise executive updates and decision briefs on adoption, impact, and risks.

Education

• Bachelor's or master's in computer science, electrical/electronic engineering, or related field; advanced ML/AI coursework or certifications preferred.

Must Have

• Experience:
  
  8‑12 years in Software engineering with either academic degree or interest in AI/ML, with experience in applying modern AI capabilities (LLMs, RAG, agents) to developer workflows at scale.

OR

• 5‑8 years in software engineering with demonstrated experience in applying modern AI capabilities (LLMs, RAG, agents) to developer workflows at scale.
• Technical depth, functional experience in one or more of the following:
  • LLMs & orchestration (prompting, tooling, evaluation, safety); agent frameworks; retrieval pipelines.
  • Toolchain fluency:
    Dev Sec Ops  tools.
  • Neural Networks, Machine…