Senior Agentic DevOps Engineer – Runtime Validation & Governance

Keysight Technologies, Inc.•Calabasas, CA

11h

About The Position

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 Initiative Keysight’s Applied AI Autonomy Initiative is developing a next-generation agentic orchestration framework that enables AI agents to reason, adapt, and coordinate across complex engineering workflows. Built on LangGraph and reinforcement-inspired feedback mechanisms, this framework transforms prompts and design intents into executable orchestration strategies that evolve autonomously through iterative simulation and validation loops. Our ambition is not merely to replicate human reasoning, but to push past human limits - enabling agentic systems to explore design spaces, optimize engineering workflows, and evolve orchestration strategies at a scale and speed no human could achieve. To make this possible, it will require robust runtime governance and regression intelligence layer — one that ensures every autonomous action is verified, reversible, traceable, and safe before it influences live engineering systems. This role defines that layer: the bridge between autonomy and operational trust, enabling production-scale AI-driven orchestration with assurance and auditability. What This Role Offers — Optimized for Agentic DevOps A foundational and highly visible role shaping how AI-generated orchestration earns operational trust across Keysight’s engineering ecosystem. The mandate to define the validation, governance, and assurance standards that determine when and how autonomous agent logic may safely enter production. Ownership of the mission-critical regression and verification pipeline, where agent actions, simulation feedback, and safety controls converge — enabling autonomy that is explainable, reversible, measurable, and audit-ready.

Requirements

PhD or 5+ years of experience in systems engineering, runtime validation, safety-critical infrastructure, or high-assurance deployment frameworks.
Proficiency with CI/CD systems (e.g., Jenkins, GitLab CI, GitHub Actions, or ArgoCD) for automation, validation, and controlled deployments.
Proven experience designing sandboxed execution environments, controlled-release and CI/CD pipelines, or validated deployment systems at production scale.
Strong proficiency in Python and C/C++, with expertise in sandbox architecture, promotion control, and rollback mechanisms.
Demonstrated ability in regression governance, continuous verification, and telemetry-driven runtime monitoring.
Proficiency with agentic AI frameworks (e.g., LangGraph, LangChain) and validation mechanisms for multi-agent orchestration and reasoning stability.

Nice To Haves

Experience with simulation orchestration, HPC systems, or digital-twin environments feeding live feedback into control loops.
Knowledge of differential validation, golden-run comparison, confidence-scored promotion models, and structured evaluation frameworks.
Background in governance frameworks for machine-generated logic or autonomous systems (e.g., AI or robotics).

Responsibilities

Lead the definition and implementation of validation, governance, and regression standards across Keysight’s agentic orchestration runtime.
Architect and deploy a continuous validation pipeline integrating feedback loops, guardrails, telemetry, and rollback systems.
Build self-diagnosing runtime logic that enables agents and infrastructure to detect, contain, and recover from instability autonomously.
Define and maintain safety certification, auditability, and reproducibility frameworks for agent-generated actions in production.
Collaborate with framework architects, safety engineers, ML researchers, and data intelligence teams to align validation infrastructure with orchestration and learning feedback systems.