Senior Machine Learning Engineer

Requirements

• At least 5 years of experience in Machine Learning or AI-related roles with a focus on financial data modeling, quantitative analysis, or algorithmic trading systems
• Proficiency in Python, with hands-on experience using libraries such as TensorFlow, PyTorch, scikit-learn, and XGBoost
• Familiarity with big data frameworks (e.g., Spark, Dask) and cloud platforms like AWS or GCP
• Proven track record of developing and deploying trading models or financial strategies
• Strong experience in time-series forecasting, financial data analysis, and feature engineering for stock market data, including technical indicators and sentiment analysis
• Expertise in hyperparameter tuning techniques, model optimization, and performance enhancement
• Solid foundation in statistics, probability, and optimization methods, with knowledge of risk management metrics such as Sharpe Ratio, Alpha, and Beta for portfolio optimization
• At least an Upper-Intermediate level of English

WILL BE A PLUS

• Experience in proprietary trading, hedge funds, or asset management firms
• Knowledge of trading platforms such as Interactive Brokers, Alpaca, or similar systems
• Knowledge of options pricing, derivatives, or quantitative trading strategies
• Familiarity with alternative data sources, including news sentiment, social media trends, and other non-traditional datasets for market analysis
• Experience with transformer models (both language and visual)
• Hands-on experience with backtesting tools like Zipline or Backtrader
• Familiarity with Docker, Kubernetes, and CI/CD pipelines for scalable model deployment

Personal Profile

• Strong critical thinking and problem-solving skills, with the ability to assess and challenge model assumptions
• Excellent communication skills for presenting complex concepts
• Ability to work both independently and collaboratively in fast-paced, dynamic environments

Responsibilities

• Collect, clean, and preprocess historical financial data, extracting meaningful features such as moving averages, RSI, and volatility indicators to enhance model performance
• Design and train predictive models (e.g., LSTM, XGBoost, Random Forests) with rigorous backtesting, hyperparameter tuning, and evaluation using metrics such as Sharpe Ratio, Sortino Ratio, and Maximum Drawdown
• Deploy models in production environments, monitor performance, address data drift through retraining, and collaborate with teams to integrate insights into trading systems while maintaining thorough documentation