<h2>About the Role</h2> <p>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.</p> <p>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.</p> <h2>Key Responsibilities</h2> <ul> <li>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.</li> <li>Build full-stack web applications: responsive, production-grade frontends using React/Next.js, backed by well-designed REST APIs and backend services.</li> <li>Design and build APIs and services that are consumed by applications, internal tools, and AI agents/LLMs, including tool-calling interfaces..</li> <li>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.</li> <li>Architect for distributed systems concerns: scalability, fault tolerance, data consistency, and service boundaries across backend and data layers.</li> <li>Apply reliability engineering practices — observability (logging, metrics, tracing), SLAs/SLOs, incident response, and post-incident reviews — to keep production systems healthy.</li> <li>Design and optimize relational database schemas, including joins, indexing, and query performance tuning.</li> <li>Build clean, well-tested, production-grade frontend and backend code that others can read, test, and extend.</li> <li>Clarify requirements upfront, surface blockers early, and communicate progress proactively without being asked.</li> <li>Collaborate openly with internal Business stakeholders, Product, QA, DevOps, and UX or designer teams; avoid working in silos.</li> </ul> <h2>Required Qualifications </h2> <p><strong><em>Education & Experience</em></strong></p> <ul> <li>Bachelor's or Master's degree in Computer Science, Engineering, or equivalent practical experience.</li> <li>8+ years of hands-on software engineering experience across full-stack development (flexible based on demonstrated strength in technical evaluation).</li> <li>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..</li> <li>Demonstrated experience owning systems designed for scale and reliability, not just feature delivery.</li> </ul> <p><strong><em>Technical Skills — Applied AI</em></strong></p> <ul> <li>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.</li> <li>Practical understanding of AI-specific considerations: latency, cost, rate limiting, and safety/guardrails when exposing systems for AI consumption.</li> <li>Experience with AI-assisted development tools (e.g., Claude Code or similar).</li> </ul> <p><strong><em>Technical Skills — Full Stack</em></strong></p> <ul> <li>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).</li> <li>Backend: Proficient in Python, writing production-grade code; hands-on experience with FastAPI or Flask (or equivalent frameworks) for building REST APIs.</li> <li>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.</li> </ul> <p><strong><em>Technical Skills — Distributed Systems & Reliability</em></strong></p> <ul> <li>Working knowledge of distributed systems concepts: service decomposition, consistency models, caching, queuing/messaging, and failure handling.</li> <li>Reliability engineering fundamentals: observability (logging, metrics, tracing), SLAs/SLOs, error budgets, and operational runbooks.</li> <li>Ability to reason about system design trade-offs and build scalable, resilient solutions under ambiguity.</li> </ul> <p><strong><em>Soft Skills</em></strong></p> <ul> <li>Proactive communication — shares updates without being asked and flags blockers early.</li> <li>Asks the right questions — clarifies requirements upfront before jumping into implementation.</li> <li>Ownership mindset — treats the project as their own rather than just completing tickets.&