About
Keysight delivers solutions in electronic design, simulation, prototyping, testing, manufacturing, and optimization for communications, automotive, energy, aerospace, defense, and semiconductor markets. Its Software and AI Labs team accelerates developer productivity, modernizes engineering workflows, and supports secure, compliant adoption of AI technologies.
Responsibilities
•Lead technical evaluations and enterprise rollouts of AI tools and platforms
•Design reusable solution patterns, including prompt libraries, RAG architectures, and agent workflows, to improve coding, testing, documentation, and planning
•Develop reference implementations integrating AI into DevSecOps toolchains
•Evaluate emerging AI paradigms, such as MCP and agentic frameworks, and guide their safe adoption
•Architect end-to-end AI solutions across cloud, on-premises, and hybrid environments
•Optimize AI integrations for performance, cost efficiency, and developer experience
•Collaborate with engineering and DevSecOps teams to embed AI into CI/CD, testing, and release processes
•Translate corporate AI governance into practical engineering guardrails
•Align AI practices with ISO/IEC 42001, NIST AI RMF, SOC2, and OWASP API Security principles
•Implement secure data handling, model access controls, and vendor usage standards
•Engage engineering leaders and IT stakeholders to drive adoption programs
•Manage vendor evaluations, pilots, and compliance alignment
•Provide executive-level updates on AI adoption impact, productivity gains, and risks
Requirements
•Bachelor's or Master's degree in Computer Science, Engineering, or a related field
•5–10+ years of experience in software engineering
•Hands-on experience applying modern AI technologies, including LLMs, RAG, and agent frameworks, to real-world engineering workflows
•Experience integrating AI into DevSecOps environments and CI/CD toolchains
•Working knowledge of secure software development practices and practical security controls
•Strong communication skills and experience influencing cross-functional teams
Nice to have:
•Experience defining enterprise engineering KPIs and productivity metrics
•Exposure to ISO/IEC 42001, NIST AI RMF, SOC2, or similar compliance frameworks
•Experience with MLOps pipelines and data engineering for AI (ETL, embeddings, vector databases)
•Prior experience driving enterprise AI enablement programs
•Experience in large, global engineering organizations
Conditions
No conditions specified
