Keysight Technologies is at the forefront of technology innovation, delivering breakthroughs in electronic design and testing. They are seeking a Principal AI Engineer to guide the safe adoption of AI tools, develop solutions for software development processes, and ensure compliance with AI governance.
Responsibilities:
- 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 DevSecOps 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/guilds 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
- 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 toolchains
- Evaluate upcoming AI technology concepts (MCP, Agentic AI) and guide Software Engineering teams to safe adoption
- 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
- 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
- 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
Requirements:
- Bachelor's or master's in computer science, electrical/electronic engineering, or related field; advanced ML/AI coursework or certifications preferred
- 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: DevSecOps tools
- Neural Networks, Machine learning, MLOps
- Data engineering for AI use cases (extract-transform-Load (ETL); embeddings, vector stores), and ML Ops practices
- Security & compliance: Working knowledge of Security; experience in implementing practical security controls for developer teams
- Communication & leadership: Strong ability to drive change, run enablement programs, and influence stakeholders across global teams