AI/ML Research Intern – Quantitative Trading & Financial Machine Learning

Selected intern's day-to-day responsibilities include: 1. Work on research and development of AI/ML models for quantitative trading and financial market analysis. 2. Work with historical and intraday stock-market/time-series data using Python, Pandas, and NumPy. 3. Develop, train, test and optimize machine-learning models using algorithms such as XGBoost, LightGBM, Random Forest and other suitable techniques. 4. Perform feature engineering using price, volume, volatility, technical indicators, market/sector context and other financial features. 5. Work on time-series validation, walk-forward testing, backtesting and prevention of data leakage/overfitting. 6. Evaluate models using accuracy/win rate, precision, confidence, Sharpe ratio, profit factor, maximum drawdown, average return per trade and other relevant metrics. 7. Research and integrate news sentiment, market sentiment and other contextual information where useful. 8. Assist in developing AI-generated trading signals including probability/confidence, risk, entry, target, stop-loss and NO-TRADE decisions. 9. Develop or integrate REST APIs using FastAPI and assist with connecting ML models to the product backend/UI. 10. Maintain clean, reusable, version-controlled code using Git/GitHub and document experiments, models, and results. 11. Research new approaches, compare models objectively, and continuously improve the existing ML pipeline. 12. Work closely with the founder and existing technical team on developing the MVP of an AI-based trading analytics platform. This is a 6-month, hands-on R&D internship with significant ownership of a real-world AI/ML FinTech product. High-performing interns may be considered for a long-term role based on performance, product requirements, company growth, and availability of positions. Candidates who can commit seriously for the full internship duration will be preferred.

, company growth, and availability of positions. Candidates who can commit seriously for the full internship duration will be preferred.