AI/ML Engineer

A fintech startup is seeking a highly skilled Machine Learning/ AI Engineer to help design, train, and optimize predictive models for the "Magnificent 7" stocks (NVDA, AAPL, META, TSLA, GOOG, MSFT, and AMZN).

About the Project

An innovative fintech company focused on building AI-driven trading solutions with mission – to develop cutting-edge strategies that deliver superior risk-adjusted returns.

Role Overview

As a Machine Learning/AI Engineer, you will work on developing, training, and deploying advanced AI/ML models such as LSTMs, XGBoost, and other algorithms tailored to optimize trading strategies. The ultimate goal is to achieve a Sharpe ratio exceeding 3 on these stocks, leveraging up to 10 years of historical market data on daily, 4-hour, or hourly timeframes.

Key Responsibilities

Data Processing & Management:

• Collect, clean, and preprocess up to 10 years of historical market data for NVDA, AAPL, META, TSLA, GOOG, MSFT, and AMZN.
• Engineer features such as moving averages, RSI, volume trends, volatility indicators, and others to enhance model inputs.

Model Development & Training:

• Design, train, and evaluate models including LSTM, XGBoost, Random Forests, and ensemble methods.
• Optimize models for risk-adjusted returns, ensuring a Sharpe ratio > 3 is achievable.
• Experiment with alternative architectures, including transformer models, for time-series prediction.

Hyperparameter Tuning:

• Conduct extensive hyperparameter tuning to enhance model performance using grid search, Bayesian optimization, or other methods.

Evaluation & Validation:

• Backtest models rigorously with historical data and validate forward-looking performance.
• Assess model performance using statistical metrics such as Sharpe ratio, Sortino ratio, and maximum drawdown.

Deployment & Monitoring:

• Deploy models in a production environment with minimal latency.
• Monitor real-time predictions, retrain models periodically, and address data drift.

Collaboration & Reporting:

• Work closely with the quant team and software engineers to integrate models into trading systems.
• Document research, experiments, and results clearly and concisely.

Requirements

Technical Skills:

• Proficiency in Python with expertise in libraries such as TensorFlow, PyTorch, scikit-learn,

and XGBoost.

• Strong experience with time-series forecasting and financial data modeling.
• Knowledge of feature engineering for stock market data (e.g., technical indicators, sentiment

analysis).

• Experience with backtesting frameworks and tools like Zipline or Backtrader.
• Familiarity with big data technologies (e.g., Spark, Dask) and cloud platforms like AWS or GCP.
• Hands-on experience with hyperparameter tuning techniques and model optimization.

Quantitative Skills:

• Strong foundation in statistics, probability, and optimization.
• Familiarity with risk management and portfolio optimization metrics like Sharpe ratio, beta, and alpha.

Experience:

• Minimum 3-5 years of experience in ML/AI roles, preferably in finance or algorithmic trading.
• Proven experience in building and deploying trading models or strategies for financial markets.

Soft Skills:

• Problem-solving mindset and ability to think critically about model assumptions.
• Strong communication skills to explain complex models to non-technical stakeholders.
• Ability to work independently and collaboratively in a fast-paced environment.

Preferred Qualifications

• Master’s or Ph.D. in Computer Science, Data Science, Mathematics, or related fields.
• Previous experience in proprietary trading, hedge funds, or asset management firms.
• Knowledge of trading platforms like Interactive Brokers or Alpaca.
• Familiarity with alternative data sources (e.g., news sentiment, social media trends).