Principal AI Engineer (Software & AI Labs)

Descrição da Vaga

Requisitos

• 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
• 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

Responsabilidades

• 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
• To be successful, the role requires an engineering mindset to manage, design, and execute on the following 4 pillars:
• 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 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
• 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

Benefícios