AI/ML + Quant Developer Needed — Predictive S&P 500 Dashboard (20+ Filters, Autopilot, QuantConnect

I’m looking for a full-stack AI/ML + Quant developer to build a predictive S&P 500 analytics dashboard with 20+ proprietary filters, real-time institutional flow tracking, and machine-learning-driven ranking models. The platform should operate like a QuantConnect-style proprietary dashboard but focused on S&P 500 swing trading signals and institutional activity detection. The system must run on autopilot—continuously scanning, filtering, modeling, and scoring opportunities without manual input. Core Features 1. Real-Time Market Data Integration Live S&P 500 price, bid/ask, volume, volatility Block trade + dark pool activity Institutional flow aggregation Optional: plug in QuantConnect data feeds or LEAN pipelines 2. 20+ Custom Filters (Provided) Filters include: Volume spikes Momentum + acceleration Trend regime Institutional clustering Technical indicator behavior Relative strength Volatility compression/expansion Many more (full set provided after hire) Filters must feed both the dashboard and the ML model. 3. AI / Machine Learning Layer Build a predictive model that produces: Real-time trade opportunity scoring Predictive ranking for swing trading Multi-feature signals using: Block trade clusters Volume anomalies Technical indicators Institutional footprints Autocorrelation and volatility features Ability to retrain model on schedule or manually Optional: backtesting via QuantConnect’s LEAN engine 4. Proprietary Quant Dashboard (QuantConnect-Style) A web dashboard with: Real-time scanning across all S&P tickers ML scoring + composite ranking Color-coded opportunity tiers Watchlists Filter presets Signal heatmaps Institutional flow visualizations Export to CSV / Excel Autopilot mode (continuous scanning + alerting) Should have the polished, responsive feel of a hedge-fund internal dashboard. 5. Optional Integrations (Not required, but a major plus) QuantConnect (LEAN) backtesting Broker API integrations (IBKR, Alpaca, Tradier) Redis / Kafka for real-time stream handling Kubernetes or Dockerized deployment Tech Stack (Flexible) Frontend: React / Vue / Angular Backend: Python (FastAPI, Django) or Node.js ML/Quant: Python (scikit-learn, PyTorch/TensorFlow), NumPy, pandas Real-time: WebSockets, streaming APIs Experience with quant platforms or hedge-fund tooling strongly preferred Deliverables Fully functional predictive dashboard 20+ filters implemented + integrated ML scoring engine Autopilot scanning mode Real-time institutional/block trade tracking Clean documentation To Apply Please include: Examples of quant dashboards, trading systems, or ML analytics tools Your ML approach (feature engineering + model selection) Whether you have experience with QuantConnect/LEAN Estimated timeline + budget Apply tot his job