Machine Learning Engineer

Position: Machine Learning Engineer (Permanent, Contract)
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, Tensor Flow, 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 No

SQL 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