layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

LAYIQ - Senior AI Engineer, Agentic Systems at Rubrik, Palo Alto, CA

Lead product discovery alongside Product Managers and domain experts to understand real workflows, map them out, and define clear problems, baselines, and success criteria as the foundation for building agentic solutions.
Design, build, test, and deploy production agents, automations, and internal tools using coding agents, LLM APIs, and agent frameworks, integrated with Rubrik’s internal agent platform and business systems.
Turn complex processes into multi-agent workflows that carry real work and make decisions: orchestrators that qualify and routes deals, workers that move orders through approval and fulfillment under business rules, agents that monitor signals and recommend actions for customer health, or coordinated agents that handle hire-to-retire steps across HR, IT, and managers with shared state and clear handoffs.
Engineer agents for autonomy with reliability, including tool and orchestration flows, context management, caching, retries, and failure handling.
Leverage full-stack engineering across backend services, APIs, data models, interactive screens, approval UIs, and chat interfaces as the complete medium to deliver high-ownership, user-facing agent systems.
Apply solid software engineering practices to AI systems, treating prompts, skills, configs, and specs as versioned artifacts, with code review, tests, CI or CD, and staged rollouts.
Implement evaluation, guardrails, and quality control: derive evals from workflow data, establish quality gates and guardrails, integrate human-in-the-loop approvals where needed, and monitor production runs to refine agents over time.
Use measured results to decide how to evolve or retire agents, and turn successful systems into patterns and libraries that can be reused.
Experience you’ll need: Around 7 or more years of professional software engineering experience in production systems, and around 2 or more years of professional, hands-on experience building AI agents and agent powered production applications with measurable outcomes.
Strong full stack engineering background, with a track record of designing, building, and owning services end to end, including backend APIs and integrations, data models, and user facing interfaces such as approval UIs, dashboards, or chat surfaces.
A builder mindset: comfortable defining baselines and metrics, reporting results in business terms, working in a fast moving environment, and closing the loop by building, demoing, learning, and building again. If you like seeing improvements in hours saved, errors avoided, and teams unblocked, you will have plenty of chances to measure that here.
Software engineering skills and best practices: System design: break down complex problems, design APIs and data models, reason about latency, throughput, and failure modes, and choose simple, robust architectures.
Algorithms, data structures, access control permissions, and data provenance.
Spec driven development: turn vague requirements into clear problem statements, write or refine design docs, and use a Spec → Plan → Task → Code→ Converge flow so work is traceable from design to code, including when you use AI coding agents.
Execution discipline: use version control, code review, automated tests, CI or CD, staged rollouts and rollbacks, logging, metrics, and tracing, and improve systems based on issues and signals from production.
AI engineering skills and best practices: Build agents in code using LLM APIs, SDKs, and libraries, not just no code or low code tools, and have shipped agentic systems that real users rely on.
Design robust agent harnesses and multi agent orchestrations: model tools and resources, manage context and state, and keep runs reliable under real world data and load.
Establish continuous evaluation and safety mechanisms: turn production failures into test cases, gate changes on eval suite results, and enforce guardrails across agent workflows.
Exercise clear judgment on AI fit: decide when a problem needs an agent versus a simpler automation or no build, and make tradeoffs around model choice, latency and cost, context strategy, and autonomy using data rather than intuition.

Senior AI Engineer, Agentic Systems

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