Machine Learning Engineer

Machine Learning Engineer / ML Engineer

Machine Learning Development

• Design and implement machine learning models for financial applications, with a focus on pricing and risk analytics
  
  
• Build scalable ML pipelines for processing large-scale financial data
  
  
• Develop deep learning architectures for time series prediction, anomaly detection, and pattern recognition in market data
  
  
• Optimize model performance through advanced techniques including hyperparameter tuning, ensemble methods, and neural architecture search
  
  
• Collaborate with quants to understand pricing model requirements and identify ML opportunities
  
  
• Develop data-driven approaches to complement traditional quantitative finance models
  
  
• Support implementation of ML solutions for derivatives pricing and risk management
  
  Core Technical Skills
  
  Machine Learning Expertise:
  
  
• Deep understanding of ML algorithms (supervised/unsupervised learning, reinforcement learning)
  
  
• Extensive experience with neural networks, including RNNs, LSTMs, Transformers
  
  
• Expertise in gradient boosting, random forests, and ensemble methods
  
  
• Experience with generative models (GANs, VAEs, Diffusion models)
  
  Programming & Tools:
  
  
• Expert-level Python programming
  
  
• Proficiency with ML frameworks (PyTorch, TensorFlow, JAX)
  
  
• Experience with scikit-learn, XGBoost, LightGBM
  
  
• Strong software engineering practices and clean code principles
  
  Data & Computing:
  
  
• Experience with big data technologies (Spark, Dask)
  
  
• SQL and NoSQL databases
  
  
• Cloud platforms (AWS, GCP, Azure)
  
  Experience
  
  
• Track record of successfully deployed ML models at scale
  
  
• Experience with time series analysis and forecasting
  
  
• Experience applying ML in finance, trading, or risk management contexts
  
  
• Knowledge of stochastic processes and their applications
  
  Financial Knowledge
  
  
• General understanding of financial markets and instruments
  
  
• Basic knowledge of derivatives and their risks
  
  
• Awareness of risk management principles