Senior AI Engineer, Agentic Systems
Rubrik · Palo Alto, CA
About this role
## Senior AI Engineer, Agentic Systems ### About the team Rubrik secures and accelerates the world’s AI transformation. As AI agents reshape the enterprise, Rubrik’s Enterprise AI Incubation team proves it on Rubrik itself—building a “thinking enterprise”: applications that don’t just record business activity, but help run it. These systems learn from Rubrik’s data, act through Rubrik’s own agents, and improve with every decision. ### About the role You’ll be a senior engineer who has already shipped agents that real people rely on—and you’ll do it again at higher stakes. You’ll redesign real workflows around agent systems that carry work end to end, and ship them as the new way Rubrik operates. You’ll own your agent systems end to end: workflow understanding, architecture, code, deployment, and production operations. Instead of simply querying an API, you’ll build production-grade infrastructure around model endpoints, custom tooling, context pipelines, automated evaluations, and defensive guardrails for unstructured real-world data. You’ll also decide when a problem needs an agent vs. simpler automation (or nothing), and you have explicit authority to stop or re-scope work that won’t deliver impact. You’ll thrive in ambiguity with a low-ego mindset and collaborate effectively—using AI coding tools as core partners. ### What you’ll do - Lead product discovery with Product Managers and domain experts to understand real workflows, define problems, baselines, and success criteria. - Design, build, test, and deploy production agents, automations, and internal tools using LLM APIs, agent frameworks, and Rubrik’s internal agent platform and business systems. - Turn complex processes into multi-agent workflows that make decisions (e.g., orchestrators routing deals, workers moving orders through approvals/fulfillment, agents monitoring customer health, coordinated agents handling hire-to-retire with shared state and clear handoffs). - Engineer for autonomy with reliability: tool/orchestration flows, context management, caching, retries, and failure handling. - Apply full-stack engineering across backend services, APIs, data models, approval UIs, dashboards, and chat interfaces. - Use strong software engineering practices for AI systems: versioned artifacts (prompts/skills/configs/specs), code review, tests, CI/CD, and staged rollouts. - Implement evaluation, guardrails, and quality control: derive evals from workflow data, set quality gates, use human-in-the-loop where needed, and monitor production to refine agents. - Use measured results to evolve or retire agents, and turn successful systems into reusable patterns and libraries. ### Experience you’ll need - ~7+ years professional software engineering in production systems. - ~2+ years hands-on experience building AI agents and agent-powered production applications with measurable outcomes. - Strong full-stack background with end-to-end ownership (backend APIs/integrations, data models, and user-facing interfaces like approval UIs/dashboards/chat). - Builder mindset: define baselines/metrics, report results in business terms, iterate quickly, and close the loop by building, learning, and improving. ### Software engineering skills & best practices - System design (APIs/data models, latency/throughput/failure modes, robust architectures) - Algorithms/data structures, access control, data provenance - Spec-driven development (traceable Spec → Plan → Task → Code → Converge) - Execution di
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