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    Challenges/Arrivia/Backend Engineer, Full Stack Engineer/Agentic Software Engineer Skills Challenge

    Agentic Software Engineer Skills Challenge

    Agentic Engineering
    MCP
    Full-Stack
    API Design
    TypeScript
    Python
    Multi-Tenant Architecture
    AI Coding
    Estimated Time:
    50 minutes
    Status:Not started

    What You'll Be Doing

    The Scenario

    You are a full-stack engineer at arrivia, a global travel loyalty technology company that powers white-label booking platforms for banks, financial institutions, and membership organizations worldwide.

    arrivia's platform handles 30,000+ itineraries across 700 airlines, 1M+ hotels, and 30,000 rental car locations. Partners integrate arrivia's booking engine, loyalty currency, and marketing tools into their own branded experiences — meaning arrivia operates a multi-tenant, white-label architecture where partner-specific configuration, branding, and pricing rules must coexist on a shared platform.

    Your team has been tasked with building a new internal service: an Agentic Travel Recommendations API. This service will allow AI agents (powered by tools like Claude Code and MCP integrations) to query a member's travel history, loyalty tier, and partner-specific rules to generate personalized travel recommendations. The goal is to power a new 'AI Concierge' feature that partner brands can embed in their booking portals.

    Here is what you know:

    • The member data service already exists as a RESTful API (you can mock it). It returns: member ID, loyalty tier (Silver/Gold/Platinum), travel history (last 5 bookings with destination, dates, and booking type), and partner ID.
    • Partner-specific rules vary: some partners cap recommendations at 3 per session; others allow unlimited. Some partners exclude cruise offers entirely. These rules are stored in a partner configuration service (you can mock this too).
    • The AI agent will call your service via MCP — your API must expose endpoints that an AI agent can discover and invoke through a Model Context Protocol server.
    • Read the provided constraints carefully — they define what you can and cannot change.

    Constraints

    • Existing infrastructure only: Your service must work within arrivia's current cloud and technology stack (AWS/Azure services, containerized deployment). Do not propose a new infrastructure layer or third-party platform that arrivia does not already use.
    • Partner configuration is read-only: You cannot modify the partner configuration service. You can only read from it. Your service must respect whatever rules the partner config returns, even if they seem suboptimal.
    • Four-week first step: Scope your implementation to what a single engineer could realistically ship in four weeks. Your README should identify what ships first vs. what comes later.
    • On-call ownership: You and your team will own this service in production. Whatever you build, you are on call for at 2am. Design accordingly.

    What You'll Accomplish

    Build a working MCP server endpoint that an AI agent can discover and invoke

    Implement partner-specific rule enforcement in a multi-tenant architecture

    Design for production reliability with failure mode awareness

    Demonstrate critical evaluation and iterative use of AI coding tools

    Scope a realistic four-week delivery plan for a new internal service

    How Your Work Will Be Scored

    Agentic Engineering & Code Quality — 35% System Design & Production Thinking — 25% Problem Diagnosis & Judgment — 15% AI Fluency — 15% Resume & Background — 10%

    What to Submit

    No submission guidelines provided.

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    What You'll Be Doing
    How It's Scored
    What to Submit