Senior Applied AI Engineer

Overview

Build and own production-grade full-stack applications and APIs for Rubrik's internal Business teams, embedding AI-agentified capabilities into these internal platforms.

What you'll do

• Own features end to end, including database schema, backend services, APIs, and the 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 consumed by applications, internal tools, and AI agents/LLMs, including tool-calling interfaces.
• Build and productionize AI-agentified features, including agentic workflows, LLM-powered automation, and AI-assisted tooling.
• Architect for distributed systems concerns: scalability, fault tolerance, data consistency, and service boundaries across backend and data layers.
• Apply reliability engineering practices, including observability (logging, metrics, tracing), SLAs/SLOs, incident response, and post-incident reviews.
• 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.
• Collaborate openly with internal Business stakeholders, Product, QA, DevOps, and UX or designer teams.

What you'll need

• 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.
• Experience building or integrating AI agents with agent frameworks (e.g., LangChain, LangGraph, ADK, or equivalent) and 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).
• Strong JavaScript fundamentals; hands-on experience with React and Next.js (hooks, state management, component architecture, routing/SSR).
• Proficient in Python, writing production-grade code; hands-on experience with FastAPI or Flask (or equivalent frameworks) for building REST APIs.
• Experience with relational and non-relational databases (PostgreSQL, MySQL, SQL Server, MongoDB or equivalent); solid understanding of schema design, joins, indexing, and query optimization.
• 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.

Nice to have

• 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.