Senior Applied AI Engineer

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.

• 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.

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.

• 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.