Full-Stack AI Dev: Investment Analysis & Risk Platform (Next.js/Python/LLMs)

We're building an AI-powered platform for investment analysis and risk assessment and need a senior full-stack developer to architect and build the MVP. What you'll build: - AI Investment Analyst: Analyzes stocks, portfolios, or assets using LLMs + real-time financial data; generates reports, summaries, and investment insights - Risk Assessment Engine: Calculates risk metrics (VaR, Sharpe ratio, beta, drawdown), flags high-risk positions, and provides risk-adjusted recommendations - Interactive Dashboard: Visualizes portfolio performance, risk exposure, sector allocation, and AI-generated insights with charts and heatmaps - User Authentication & Portfolios: Secure auth, user portfolios, watchlists, and saved AI reports - AI Chat Interface: Natural language queries like "What's the risk of my tech-heavy portfolio?" or "Analyze AAPL vs MSFT" Tech Stack (preferred): - Frontend: Next.js 14+ (App Router), TypeScript, Tailwind CSS, shadcn/ui, Recharts/TradingView charts - Backend: Python (FastAPI) for AI/ML services; Node.js optional for API layer - AI/ML: OpenAI GPT-4 / Claude 3.5, LangChain for orchestration, custom Python models for risk calculations - Financial Data: Yahoo Finance API, Alpha Vantage, Polygon.io, or IEX Cloud (real-time + historical data) - Database: PostgreSQL (user data, portfolios), Redis (caching), optional Clickhouse/TimescaleDB for time-series - Risk Engine: Python (pandas, numpy, scipy, pyfolio, QuantLib) for quantitative risk metrics - Deployment: Vercel (frontend) + AWS/Railway (backend) + Docker Core Features to Implement: 1. Portfolio Upload/Build: Manual entry or API-synced holdings 2. AI Report Generation: LLM-generated investment summaries, risk narratives, and recommendations 3. Risk Metrics Dashboard: VaR, CVaR, Sharpe, Sortino, max drawdown, correlation matrices 4. Scenario Simulation: "What-if" stress testing (market crash, rate hike, sector rotation) 5. Alert System: Risk threshold breaches, price targets, AI-detected anomalies Requirements: - 4+ years full-stack development with production-grade apps - Proven experience with LLM integrations (OpenAI, Claude) and prompt engineering - Financial/quantitative background: understanding of risk metrics, portfolio theory, or prior fintech projects - Experience with financial data APIs (Yahoo Finance, Alpha Vantage, Polygon, etc.) - Strong TypeScript, React, Python, and SQL skills - Security-first mindset: OAuth, JWT, encryption, input validation - Clean GitHub or portfolio with relevant projects Nice to Have: - Experience with time-series databases or financial data processing - Background in quantitative finance, risk modeling, or CFA-level knowledge - Familiarity with RAG for analyzing large financial documents (10-Ks, earnings calls) - Knowledge of banking compliance (SEC, FINRA, MiFID II awareness) - Experience with real-time data (WebSockets, streaming APIs)