Full-Stack Developer for AI Doc Analytics
Краткое
Freelancer Client is hiring: Full-Stack Developer for AI Doc Analytics.
Location: Remote
Job Title: Full-Stack Developer (Lovable, Supabase & AI Integration) – 2-Week Sprint
What you'll do:
• AI Pipeline Integration: Build Supabase Edge Functions to handle file parsing, structured metadata extraction via Claude 4.6, and vector embedding generation via Voyage AI*.
• Vector Search & DB Optimization: Implement semantic search using pgvector and an HNSW index via Supabase RPC functions. Finalize relational database schemas, foreign keys, and strict Row Level Security (RLS) policies.
• Frontend Completion: Connect existing Lovable UI components to the Supabase backend (file upload states, active filtering, and dynamic dashboards).
• Performance Engineering: Implement asynchronous execution patterns (EdgeRuntime.waitUntil) to handle heavy AI workloads without hitting API Gateway timeouts.
• ### Technical Stack:
• Frontend: React, Next.js, Tailwind CSS (Lovable workflow)
• Backend: Supabase (Auth, Storage, Database, Edge Functions)
• Database: PostgreSQL, pgvector, HNSW indexing, PL/pgSQL
Skills: PHP, Java, NoSQL Couch & Mongo, MySQL, PostgreSQL, Full Stack Development, Next.js, AI Development
Budget: $250–$750 USD
Source: Freelancer Client via Remote / Online. Apply on the source website.
Оригинал
Job Title: Full-Stack Developer (Lovable, Supabase & AI Integration) – 2-Week Sprint
Project Overview:
We are finalizing an AI-powered document intelligence and analytics library. The frontend skeleton is built using Lovable (Next.js/React), and the backend is powered by Supabase (PostgreSQL). We need an experienced, fast-moving full-stack developer to connect the UI, optimize database structures, integrate advanced AI APIs, and complete the production deployment within a strict 2-week timeline.
Key Responsibilities:
AI Pipeline Integration: Build Supabase Edge Functions to handle file parsing, structured metadata extraction via Claude 4.6, and vector embedding generation via Voyage AI.
Vector Search & DB Optimization:* Implement semantic search using pgvector and an HNSW index via Supabase RPC functions. Finalize relational database schemas, foreign keys, and strict Row Level Security (RLS) policies.
Frontend Completion:* Connect existing Lovable UI components to the Supabase backend (file upload states, active filtering, and dynamic dashboards).
Performance Engineering:* Implement asynchronous execution patterns (EdgeRuntime.waitUntil) to handle heavy AI workloads without hitting API Gateway timeouts.
Technical Stack:
Frontend:* React, Next.js, Tailwind CSS (Lovable workflow)
Backend:* Supabase (Auth, Storage, Database, Edge Functions)
Database:* PostgreSQL, pgvector, HNSW indexing, PL/pgSQL
APIs:* Anthropic Claude (4.6), Voyage AI
Project Timeline & Deliverables:
Days 1–4:* Database schema finalization, constraint mapping, and RLS security audit.
Days 5–9:* Core asynchronous processing pipeline (Upload > AI Extraction > Embedding > DB Storage).
Days 10–12:* Frontend-to-backend integration, search filters validation, and UI state tuning.
Days 13–14:* End-to-end testing, error handling optimization, and final deployment.
Engagement: Fixed-fee, 2-week contract. Requires daily async progress updates and high availability.
Локация & Details
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