Backend Engineer Needed: Sub-1-Second Caching, Pricing Algorithm & UI Polish (React Native / Node.js

Project Budget: $500 (Strictly Milestone-Based) CRITICAL REQUIREMENT BEFORE APPLYING: Payment for this project is strictly tied to real-world performance metrics. Milestone 1 requires a live stopwatch test on a mobile device showing sub-1-second cache loading on repeat lookups. If you do not have deep experience with high-speed local database architecture and caching models, do not apply. We measure deliverables with a stopwatch, not excuses. Project Overview & The Long-Term Vision: I am building Reseller Bro, a powerful mobile utility application designed for on-the-go resellers to instantly analyze product values, get a FLIP/SKIP verdict, and SAVE to a digital cart in seconds. This app is just the initial foundation—the engineer who successfully delivers this backend infrastructure will have the opportunity to partner with us long-term to build out our entire ecosystem, including advanced B2B inventory management tools and our wearable AR glasses workflow (Bro Lens). The front-end user interface and layout are already mostly complete. We are anchoring our backend data pipeline to a high-speed eBay API model that automatically calculates smart market estimates for other secondary platforms. We need an expert developer to clean up our database cache, implement a tier-condition formula, handle minor UI adjustments, and add high-energy audio/vibration triggers. The Core Tasks & Milestone Payment Structure: Milestone 1: Sub-1-Second Database Caching ($100 Escrow) The Issue: The previous build incorrectly forced live AI image recognition to run on every single scan, causing a 7–8 second delay even on repeat lookups. The Fix: You must implement a proper local database caching layer (e.g., SQLite). The first initial scan of an item can take up to 7 seconds to run the AI workflow and fetch the initial marketplace data. However, on any repeat scan, the app must skip the AI image recognition entirely, read a cached unique text identifier/key, and instantly pull the results from the internal database in under 1 second. Milestone 2: Data Engine & Percentage-Based Estimation Matrix ($200 Escrow) The Fix: Connect the backend cleanly with the official eBay Browse API using our developer keys. Target Marketplaces: The app displays valuation metrics for four core platforms: eBay, Depop, Grailed, and Poshmark. Condition Matrix & Platform Estimation Formula: Because eBay utilizes a wide variety of specific conditions across different categories, you will build an automated mapping and calculation formula. The app will pull raw condition data initially from the eBay API, group it cleanly into our 3-tier user system, and then use those baselines to instantly calculate the estimated market values for the other three platforms (Depop, Grailed, and Poshmark). New Tier: Dynamically maps all brand-new and pristine variations data directly, including: "New", "Excellent", "Excellent - Refurbished", "Open box", "New with box", "New with defects", and "New without box". Good Tier: Dynamically maps all standard pre-owned and quality-certified variations data directly, including: "Very Good", "Good", "Used", "Very Good - Refurbished", "Good - Refurbished", "Pre-Owned", and "Certified Pre-Owned". Poor Tier: Maps heavy-wear options directly, such as "For parts or not working" or "Fair". If a specific item category lacks a true "poor" marketplace data option, the engine must automatically fall back to calculate a custom percentage markdown (e.g., 40% less) relative to that item's "Good" tier baseline. The results from these three tiers will automatically calculate estimated market values for Depop, Grailed, and Poshmark using an internal background multiplier. If a user wants to check the exact live screen on those blocked platforms, tapping a platform tile will trigger a direct, one-tap deep link search into that specific app or web page. Smart Category Specifications (Vehicle & Electronics Handling): If the AI detects an image of a Vehicle (cars, trucks) or high-value Electronics, the app must dynamically generate a quick-spec form for the user to confirm/fill in (Vehicles: Make, Model, Year, Mileage; Electronics: Brand, Model, Capacity). This structured data must be fed directly into the pricing API for precise accuracy. Milestone 3: UI Redesigns, Audio/Haptics, Live Deployment & Final Handoff ($200 Escrow) UI Tweaks: Implement minor visual layout edits and updates to a few existing UI screens to align with this new calculation model. This includes ensuring tiles are accurately labeled as "Estimated Value," verifying the condition buttons display perfectly, adjusting the selling platform logos/designs, and making a few structural changes to the "Saved Items" page (I will go over the exact design changes with you directly). Haptics & Custom Audio Cues: Implement device vibration triggers (via Web Vibrations API) to pulse the device exactly when a verdict hits the screen. Integrate custom short audio sound bites that trigger instantly on the verdict display: a high-energy "YEAH!" (Lil Jon style) sound bite for a FLIP verdict, and a "HELL NO!" sound bite for a SKIP verdict. Live Deployment: Once the final features are fully approved, you will be responsible for successfully deploying the production build live onto our hosting account so the application is fully operational. Clean up the codebase and hand over the complete, finalized source repository. Requirements: Deep expertise in backend optimization, API data pipelines, and high-speed local database caching. Proficiency in mobile development frameworks, front-end audio integration, and Node.js. Strong communication skills. You will work with an existing repository and must provide clean, documented code. To Apply: Please reply explaining exactly how you will structure the local database cache so that a repeat scan completely bypasses the image-recognition API step to hit the sub-1-second mark. Copy and paste this right onto Upwork. It has every single feature, condition category, and protective barrier built in!