Senior Applied AI Engineer at Rubrik

About the Role

We are looking for a Senior Applied AI Engineer to join Rubrik's Applied AI team, building production-grade, full-stack applications and APIs for our internal Business teams while embedding AI-agentified capabilities into these internal platforms. This is a hands-on build role: you will own features end to end — from database schema through backend services and distributed systems to the user interface — and bring practical, hands-on experience building or integrating AI agents, tool-calling workflows, or LLM-powered features into production systems that our business stakeholders rely on.

The ideal candidate combines strong full-stack engineering fundamentals with a systems mindset for reliability and scale, and has spent meaningful hands-on time (1–2 years) building AI-agentified applications — not just experimenting with AI tools, but shipping agentic features in production. We are targeting 8+ years of overall software engineering experience; this is flexible for candidates who demonstrate strong depth across full-stack, distributed systems, and applied AI during technical evaluation.

Key Responsibilities

• Own features end to end — database schema, backend services, APIs, and frontend (React/Next.js) user interface — operating as an owner rather than a ticket-taker.
• Build full-stack web applications: responsive, production-grade frontends using React/Next.js, backed by well-designed REST APIs and backend services.
• Design and build APIs and services that are consumed by applications, internal tools, and AI agents/LLMs, including tool-calling interfaces..
• Build and productionize AI-agentified features — agentic workflows, LLM-powered automation, and AI-assisted tooling — grounded in 1–2 years of hands-on experience with these systems.
• Architect for distributed systems concerns: scalability, fault tolerance, data consistency, and service boundaries across backend and data layers.
• Apply reliability engineering practices — observability (logging, metrics, tracing), SLAs/SLOs, incident response, and post-incident reviews — to keep production systems healthy.
• Design and optimize relational database schemas, including joins, indexing, and query performance tuning.
• Build clean, well-tested, production-grade frontend and backend code that others can read, test, and extend.
• Clarify requirements upfront, surface blockers early, and communicate progress proactively without being asked.
• Collaborate openly with internal Business stakeholders, Product, QA, DevOps, and UX or designer teams; avoid working in silos.

Required Qualifications

Education & Experience

• Bachelor's or Master's degree in Computer Science, Engineering, or equivalent practical experience.
• 8+ years of hands-on software engineering experience across full-stack development (flexible based on demonstrated strength in technical evaluation).
• 1–2 years of hands-on experience building, integrating, or operating AI-agentified applications (agentic workflows, tool-calling, RAG, or LLM-powered features) in production..
• Demonstrated experience owning systems designed for scale and reliability, not just feature delivery.

Technical Skills — Applied AI

• 1–2 years of hands-on experience building AI-agentified applications, including agent frameworks (e.g., LangChain, LangGraph, ADK, or equivalent), tool/function-calling for LLMs.
• Practical understanding of AI-specific considerations: latency, cost, rate limiting, and safety/guardrails when exposing systems for AI consumption.
• Experience with AI-assisted development tools (e.g., Claude Code or similar).

Technical Skills — Full Stack

• Frontend: Strong JavaScript fundamentals; hands-on experience with React and Next.js for building modern, production-grade web applications (hooks, state management, component architecture, routing/SSR).
• Backend: Proficient in Python, writing production-grade code; hands-on experience with FastAPI or Flask (or equivalent frameworks) for building REST APIs.
• Database: Experience with relational and non-relational databases (PostgreSQL, MySQL, SQL Server, MongoDB or equivalent); solid understanding of schema design, joins, indexing, and query optimization.

Technical Skills — Distributed Systems & Reliability

• Working knowledge of distributed systems concepts: service decomposition, consistency models, caching, queuing/messaging, and failure handling.
• Reliability engineering fundamentals: observability (logging, metrics, tracing), SLAs/SLOs, error budgets, and operational runbooks.
• Ability to reason about system design trade-offs and build scalable, resilient solutions under ambiguity.

Soft Skills

• Proactive communication — shares updates without being asked and flags blockers early.
• Asks the right questions — clarifies requirements upfront before jumping into implementation.
• Ownership mindset — treats the project as their own rather than just completing tickets.
• Transparency — speaks up when something is unclear instead of making assumptions.
• Strong analytical mindset to break down complex or ambiguous problems.

Preferred Qualifications

• Experience with agentic frameworks (ADK, LangGraph, or similar) beyond individual projects — production deployment experience preferred.
• Familiarity with API documentation standards (Swagger/OpenAPI).
• Basic DevOps awareness (Docker, CI/CD) and Git/version-control best practices.
• Testing experience across unit and integration levels.
• Experience with modern web app tooling — component libraries (e.g., Tailwind, MUI) and state management (e.g., Redux, Zustand).
• Exposure to cloud-native development (AWS, Azure, or GCP), including containers and infrastructure as code.

Join Us in Securing and Accelerating the World's AI Transformation

Rubrik (RBRK), the Security and AI Operations Company, leads at the intersection of data protection, cyber resilience, and enterprise AI acceleration. Rubrik Security Cloud delivers complete cyber resilience by securing, monitoring, and recovering data, identities, and workloads across clouds. Rubrik Agent Cloud accelerates trusted AI agent deployments at scale by monitoring and auditing agentic actions, enforcing real-time guardrails, fine-tuning for accuracy and undoing agentic mistakes.

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