Senior Applied AI Engineer – Developer Productivity & SDLC
Confirmed on the employer's own hiring board on Oct 12, 2026. First seen by Alion on Feb 9, 2026.
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.
About the Team
Software and AI Labs (SAL) drives innovation across Keysight’s global software engineering organization. The team focuses on accelerating developer productivity, modernizing engineering workflows, and ensuring secure, compliant adoption of emerging AI technologies.
You will join a cross-functional group working closely with Engineering, DevSecOps, IT, and Architecture teams worldwide. The culture is experimental, impact-driven, and focused on safe, scalable AI enablement across enterprise software development.
About the Role
This role sits at the intersection of AI, software engineering, and enterprise transformation.
As a Senior Applied AI Engineer, you will design and implement AI-driven solutions that modernize and accelerate Keysight’s Secure Software Development Lifecycle (SDLC). You will evaluate emerging AI technologies (LLMs, RAG, agentic systems), translate governance into practical engineering standards, and build reference architectures that can scale across global engineering teams.
Your impact will directly influence developer productivity, engineering quality, and responsible AI adoption across the enterprise.
Responsibilities
AI-Led Productivity & SDLC Acceleration
Lead technical evaluations and enterprise rollouts of AI tools and platforms.
Design reusable solution patterns (prompt libraries, RAG architectures, agent workflows) to improve coding, testing, documentation, and planning.
Develop reference implementations (“golden paths”) integrating AI into DevSecOps toolchains.
Evaluate emerging AI paradigms (e.g., MCP, agentic frameworks) and guide safe adoption.
Architecture & Integration
Architect end-to-end AI solutions spanning cloud, on-prem, 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.
Governance, Risk & Compliance
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 control, and vendor usage standards.
Stakeholder & Program Leadership
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.
Qualifications
Required Qualifications
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
5-10+ years of experience in software engineering.
Hands-on experience applying modern AI technologies (LLMs, RAG, 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 with experience influencing cross-functional teams.
Desired Qualifications
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 working in large, global engineering organizations.
Careers Privacy Statement ***Keysight is an Equal Opportunity Employer.***
AI-Led Productivity & SDLC Acceleration
Lead technical evaluations and enterprise rollouts of AI tools and platforms.
Design reusable solution patterns (prompt libraries, RAG architectures, agent workflows) to improve coding, testing, documentation, and planning.
Develop reference implementations (“golden paths”) integrating AI into DevSecOps toolchains.
Evaluate emerging AI paradigms (e.g., MCP, agentic frameworks) and guide safe adoption.
Architecture & Integration
Architect end-to-end AI solutions spanning cloud, on-prem, 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.
Governance, Risk & Compliance
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 control, and vendor usage standards.
Stakeholder & Program Leadership
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.
Required Qualifications
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
5-10+ years of experience in software engineering.
Hands-on experience applying modern AI technologies (LLMs, RAG, 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 with experience influencing cross-functional teams.
Desired Qualifications
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 working in large, global engineering organizations.
Careers Privacy Statement ***Keysight is an Equal Opportunity Employer.***