Senior Applied AI Engineer - Developer Productivity & SDLC

is on 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. Our

culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. About Keysight AI Labs

Keysight’s

AI Labs

is a global R&D group pioneering the integration of

into Keysight’s test, measurement, and design solutions. Our mission is to transform how engineers design, simulate, and validate advanced systems- from 6G and semiconductors to quantum and automotive - by embedding AI throughout our workflows.

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.

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.

Design reusable solution patterns (prompt libraries, RAG architectures, agent workflows) to improve coding, testing, documentation, and planning.

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.

Implement secure data handling, model access control, and vendor usage standards.

Engage engineering leaders and IT stakeholders to drive adoption programs.

Provide executive-level updates on AI adoption impact, productivity gains, and risks.

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.

5–10+ years of experience in software engineering.

Working knowledge of secure software development practices and practical security controls.

Experience with MLOps pipelines and data engineering for AI (ETL, embeddings, vector databases).

Experience working in large, global engineering organizations.