Professional Summary
AI Engineer, Quantitative Developer, and Technical Architect with an MSc in Computer Science (AI & Data Science, Merit) from the University of Wolverhampton. Currently Head of Quants at Quant Sigma, London, leading institutional-grade HFT operations across Forex, CFDs, indices, and cryptocurrency markets while architecting production-grade AI systems for trading intelligence and execution.
Proven track record building scalable trading platforms, deploying production ML systems processing millions of predictions daily, and developing high-frequency trading infrastructure with sub-millisecond latency. Expert in LLM integration, RAG architectures, multi-provider AI orchestration, transformer-based predictive models, and full-stack development. Over a decade of systematic trading, scaling capital 20x+ with ~100% annualised returns and disciplined risk management.
Core Technical Competencies
Professional Experience
Head of Quants | AI & Trading Systems Architect
Leading quantitative research, AI-powered trading systems, and execution infrastructure for institutional-grade HFT across traditional and crypto markets.
- Developed multi-signal HFT models: trend following, mean reversion, volatility breakout across Forex, CFDs, indices, crypto
- Implemented transformer-based predictive models for market direction forecasting (significant Sharpe ratio improvement)
- Built reinforcement learning execution system for adaptive position sizing
- Created ensemble models (XGBoost, LightGBM, TinyML) for queue-based market making
- Achieved ~100% annualised returns through volatility-based position sizing and scenario testing
- Architected low-latency execution infrastructure with millisecond performance, colocation at Equinix LD4
- Real-time market data pipeline handling WebSocket feeds from multiple exchanges
- Comprehensive backtesting framework: 5+ years tick-level data, transaction cost modelling
- Distributed ML inference pipeline: <100ms latency for real-time signal generation
- Automated risk management: real-time portfolio monitoring, volatility targeting, drawdown controls
- Built AI-assisted decision workflows using LLMs with RAG for trading research and market intelligence
- NLP sentiment analysis engine processing 500K+ articles, social media, alternative data daily
- Automated feature engineering pipeline: 100+ technical indicators, microstructure metrics
- Established MLOps practices: model versioning, evaluation, deployment patterns, ongoing monitoring
- LLM capabilities (GPT, Claude, Gemini) for market narrative analysis and alternative data
- Architected FastAPI microservices for real-time data ingestion and trading signal delivery
- PostgreSQL/TimescaleDB for time-series storage, Redis for caching and real-time state
- Docker/AWS deployment for scalable, production-grade trading infrastructure
- End-to-end platform architecture across data ingestion, API integration, backend services
Quant and Algorithmic Trader
- Achieved Sharpe ratio 1.5+ (Forex/CFDs) and 2–6 (crypto), generating 20x+ profit through strategic trades and arbitrage
- Applied sophisticated risk management using Python for predictive models and portfolio optimisation
- Conducted extensive backtesting using MT4/5 and Python-based automation with AI-powered insights
- Developed comprehensive blockchain and cryptocurrency expertise: DeFi protocols, yield farming, liquidity provision
- Built algorithmic trading bots for crypto markets, cross-chain arbitrage strategies
- Authored investment-grade quantitative performance reports for family office investments
Senior Product Manager (Technical) / Web Manager
- Led strategic account management and coordinated with international merchants for brand positioning
- Developed and maintained project plans, cost estimation and resource allocation
- Oversaw inventory management, supply chain coordination, and pricing structure
- Conducted comprehensive market analysis to support business operations
Senior Operations Manager
- Oversaw strategic and operational aspects within the telecommunications software sector
- Achieved 25% annual growth rate through strategic leadership
- Led multi-disciplinary team, implementing development programs
- Managed entire project lifecycle for custom software solutions
Notable Achievements
Systematic Trading (2013 – Present)
- Scaled trading capital 20x+ over a decade of systematic trading
- ~100% annualised returns depending on market conditions
- Maintained controlled drawdowns through disciplined volatility-based position sizing
- Active across Forex, CFDs, indices, cryptocurrency, and derivatives markets
- Early Bitcoin investor since $80 (2013)
Technical Leadership
- Built and deployed production AI systems for trading intelligence
- Architected distributed ML inference pipelines serving real-time predictions
- Developed RAG systems and LLM-powered trading research platforms
- Led technology transformation from traditional to AI-augmented trading
Research & Selected Projects
Click project to view full image
Novel architecture combining LLMs with real-time HFT for multi-asset trading. Custom transformer model with attention mechanisms. 65% improvement in Sharpe ratio over baseline. Tech: Python, PyTorch, CCXT, Docker, TimescaleDB, Ray.
LSTM/GRU/XGBoost ensemble with 50+ technical indicators. 78% directional accuracy on out-of-sample data.
Real-time cryptocurrency sentiment monitoring using NLTK and TextBlob across Twitter, Reddit, and financial news.
Multi-asset backtesting engine with 5+ years tick-level data, transaction cost modelling, and walk-forward optimisation. Python, QuantConnect.
Live portfolio monitoring with volatility targeting, drawdown controls, and P&L attribution. FastAPI + React + TimescaleDB.
Distributed ML inference pipeline generating real-time trading signals with <100ms latency across multiple asset classes.
Education
Coursework: Deep Learning, NLP, Machine Learning, Neural Networks, Distributed Systems, MLOps