Fractional CTO / Senior Python ML Engineer — AI-Driven Model

We're a professional trader building a private, AI-driven market research engine, and we're looking for a senior Python / machine learning engineer to act as Fractional CTO across a two-phase build. Phase 1 (this contract): a Financial Market Signal Backtesting Dashboard — a clean measurement harness for trend, momentum, and multi-signal models with walk-forward backtesting. Scope: • Python project + GitHub repo • Historical market-data ingestion (ETFs via yfinance / market-data APIs, SQL storage) • Baseline models: 200-day trend, time-series & cross-sectional momentum, equal-weight multi-signal • Walk-forward backtesting with look-ahead-bias controls • Evaluation metrics: hit rate, Brier score, Sharpe, Sortino, Calmar, max drawdown, turnover, calibration • A dashboard comparing signals and model performance Stack: Python, pandas, NumPy, SQL, Python-based dashboard. Phase 2 (follow-on): we extend the harness into the AI layer — an OODA / weight-of-evidence engine that scores competing market hypotheses. This involves LLM-based signal processing, an AI agent workflow over financial research and macro data, model evaluation / observability (LLMOps), and potentially fine-tuning and RAG over our research library. Strength here shapes who we keep on as Fractional CTO. You are: • A senior Python engineer with quantitative finance / backtesting experience • Strong in machine learning and statistical evaluation • Comfortable owning architecture as a Fractional CTO — evaluations, regression tests, observability, clean handover • Bonus for Phase 2: AI agents, LLMs, LLMOps (LangChain / LangGraph, fine-tuning, RAG)