AI Engineer (Fintech)

TL;DR

AI Engineer (Fintech): design, develop, and deploy machine learning models and AI-driven solutions to enhance trading strategies, portfolio optimization, risk management, and financial forecasting with an accent on deep learning, NLP, and reinforcement learning techniques. Focus on integrating AI models into trading and risk systems, feature engineering, hyperparameter tuning, and ensuring model explainability, robustness, and regulatory compliance.

Location: London

Salary: £150k base + bonus + benefits

Company

Leading investment management firm working at the intersection of AI and finance.

What you will do

• Develop and optimize ML models for financial market predictions, portfolio management, and risk assessment.
• Build AI-powered analytics tools and data pipelines for large-scale financial datasets.
• Research and implement deep learning, NLP, and reinforcement learning for investment strategies.
• Integrate AI models with quantitative researchers and traders into trading and risk systems.
• Improve model performance via feature engineering, hyperparameter tuning, and validation.
• Optimize execution using distributed computing and cloud infrastructure.

Requirements

• Bachelor’s, Master’s, or PhD in Computer Science, Machine Learning, Mathematics, or related field.
• Strong Python skills and experience with AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Experience with financial data and applying AI/ML in investment or trading context.
• Familiarity with time-series forecasting, NLP, reinforcement learning, and generative AI.
• Proficiency in data engineering, big data processing (Spark, Dask, Kafka), and cloud (AWS, GCP, Azure).
• Strong statistical modeling, probability, and optimization knowledge.
• Ability to collaborate with quant researchers, portfolio managers, and tech teams.

Nice to have

• Experience in hedge fund, asset management, or proprietary trading firm.
• Knowledge of market microstructure, options pricing, and quantitative trading strategies.
• Familiarity with C++ or Rust for low-latency AI execution.
• Prior experience in automated trading systems, alternative data, or AI-driven portfolio optimization.