Professional Summary
Head of Quants at Quant Sigma (London), running institutional-grade HFT operations across Forex, CFDs, indices and cryptocurrency. MSc Computer Science (AI & Data Science, Merit) from the University of Wolverhampton, with a dissertation on LLM-Augmented High-Frequency Trading, fusing transformer-based market understanding with sub-millisecond execution.
Proven track record building and operating AI-native trading infrastructure: multi-provider LLM routing across OpenAI, Anthropic Claude, Google Gemini, DeepSeek, Qwen, OpenRouter, NVIDIA and Cloudflare, benchmarked with TTFT p50/p95 analytics for latency-critical alpha research; production RAG systems with policy guardrails and human-review escalation for regulated decision flows; distributed ML inference pipelines serving tons of predictions daily at sub-1ms latency; transformer-based predictive models, reinforcement-learning execution, and full-stack platform delivery across AWS, GCP and Cloudflare.
Led the design, training and deployment of our in-house trading-grade LLM runtime and a high-speed portfolio chatbot fully developed by Pak. The stack delivers 5x to 60x faster first-token and response latency than mainstream ChatGPT, Gemini, Claude and DeepSeek on comparable workloads, enabling sub-millisecond alpha annotation, guardrailed reasoning and evidence-packed signal generation fully inside the firm's own runtime.
Over a decade of systematic trading, scaled capital 20x+ with ~100% annualised returns through disciplined volatility-based sizing and cross-asset arbitrage. Securities & Futures Commission of Hong Kong Exams (Papers 1/7/8/12). Harvard CS50x, Google MLOps and Gemini Certified Educator. The rare engineer who builds institutional-grade HFT infrastructure and production LLM runtimes from the same keyboard.
Notable Achievements
Systematic Trading (2013 – Present)
- Scaled trading capital 20x+ over a decade of disciplined systematic trading
- ~100% annualised returns; controlled drawdowns via volatility-based sizing
- Forex, CFDs, indices, crypto, derivatives · Early Bitcoin investor since $80 (2013)
LLM Infrastructure & AI Platforms
- Built LLM Arena multi-provider fast-inference benchmark with TTFT p50/p95 analytics
- Shipped audit-grade AI decision runtime with policy engine, guardrails & human-review gate
- Production LLM routing at sub-100ms TTFT, automatic fail-over & cost ledger
Technical Leadership
- Led tech transformation from traditional to AI-augmented trading
- Architected distributed ML inference pipelines serving tons of predictions daily at sub-1ms latency
- Mentored 6+ engineers · multi-cloud (AWS · GCP · Cloudflare) with finance-compliance rigour
Research & Publications
- MSc dissertation: LLM-Augmented High-Frequency Trading, novel LLM×HFT fusion
- 65% Sharpe-ratio improvement over baseline achieved in MSc research
- Investment-grade quant performance reports for family-office mandates
Core Technical Competencies
Professional Experience
Head of Quants · AI-Native HFT & Systems Architect
Leading quantitative research, AI-powered trading systems and execution infrastructure for institutional-grade HFT across traditional and crypto markets. Built an AI-native alpha-research and decision-runtime stack alongside the core low-latency execution platform.
- Multi-signal HFT models across Forex, CFDs, indices and crypto: trend following, mean reversion, volatility breakout, queue-based market making, XGBoost / LightGBM ensembles for microstructure signal blending
- Implemented transformer-based predictive models for market-direction forecasting, material Sharpe-ratio uplift over baseline
- Reinforcement-learning execution for adaptive position sizing and inventory control
- Achieved ~100% annualised returns via volatility-based sizing, regime switching and scenario stress tests
- Low-latency execution infra with millisecond performance, colocation at Equinix LD4 & AWS London region; real-time WebSocket market-data pipeline with deterministic ingestion
- Distributed ML inference pipeline: <100ms latency for real-time signal generation
- Event-driven order-book reconstruction with lock-free ring buffers and kernel-bypass networking; sub-microsecond jitter budget on the hot path
- Backtesting framework: 5+ years tick-level data, transaction-cost modelling, walk-forward optimisation; real-time portfolio monitoring, volatility targeting, drawdown kill-switches
- Built an internal multi-provider LLM routing layer with cost / latency / risk policy and automatic fail-over across OpenAI, Anthropic, Google Gemini, DeepSeek, Qwen, OpenRouter, NVIDIA and Cloudflare
- Designed a prompt registry with version pinning, policy prompts and evidence-pack assembly for alpha research and decision support
- Implemented a guardrail & rules engine with risk scoring and human-review escalation for regulated-grade decision workflows
- Built the LLM Arena multi-provider fast-inference benchmark (TTFT p50/p95 analytics) and an audit ledger tying every AI call to workflow, policy, cost and support lineage
- NLP sentiment engine ingesting 500K+ articles / social / alt-data daily; RAG over proprietary research and earnings corpora with grounded citation
- MLOps discipline: model registry, evaluation suites, canary deployment, drift monitoring, per-strategy cost ledger
- Led end-to-end design, training and deployment of the in-house trading-grade LLM runtime and Pak's high-speed portfolio chatbot for decision support
- Curated a proprietary trading-intelligence corpus (multi-billion tokens) spanning order-book transcripts, earnings calls, research notes and regulatory filings; mixture-of-experts routing with task-specialised heads for alpha reasoning, risk critique and compliance review
- Achieved 5x to 60x faster TTFT and response latency vs mainstream frontier LLMs on comparable workloads through custom inference kernels, speculative decoding and trading-corpus fine-tuning
- Integrated the in-house runtime into the firm's decision stack for sub-millisecond alpha annotation, guardrailed reasoning and evidence-packed signal generation fully inside the firm's own perimeter
Lead AI Engineer & Technical Architect · Quant & Algorithmic Trader
Dual-track: spearheaded AI transformation (Dec 2021 – Dec 2024) establishing the firm's AI/ML practice and intelligent systems processing tons of predictions daily; and throughout the full tenure ran a personal systematic-trading programme generating institutional-grade returns.
- Designed and deployed distributed ML inference system achieving sub-1ms latency for real-time predictions, serving tons of predictions daily
- Built comprehensive RAG system (LangChain + vector DB) reducing information retrieval time by 85% while maintaining 94% accuracy
- Developed end-to-end MLOps pipeline with automated retraining + A/B testing, improving model performance 40% Q-over-Q
- Implemented transformer-based sentiment analysis processing 500K+ documents daily for market intelligence
- Established AI/ML best practices, conducted architecture reviews and mentored a team of 6 engineers
- Achieved Sharpe 1.5+ (Forex/CFDs) and 2–6 (crypto); generated 20x+ capital growth via strategic directional trades, arbitrage and volatility harvesting
- Python-based predictive models and portfolio optimisation with rigorous volatility-based risk sizing
- Backtesting with MT4/MT5 + Python automation + AI-driven feature discovery
- Comprehensive blockchain / cryptocurrency expertise: DeFi protocols, yield farming, liquidity provision, cross-chain arbitrage
- Authored investment-grade quantitative performance reports for family-office investments
- Enterprise Document Management System: multi-tenant SaaS, 100+ clients, 1M+ daily API requests, 99.9% uptime
- Analytics Dashboard Platform: real-time visualisation, 10GB+ daily data, 60% reduction in report generation time
- E-commerce Integration Suite: multi-platform API layer, automated inventory sync, 5+ payment-gateway integrations
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
Research & Selected Projects
Novel architecture fusing LLM narrative understanding with real-time HFT across Forex, CFDs, indices and crypto. Custom transformer with attention heads over microstructure features (order-book depth, trade-flow imbalance, spread volatility), ensemble gating and latency-aware inference scheduling that down-routes heavy heads on tight time budgets. 65% Sharpe-ratio uplift over baseline on 5-year out-of-sample evaluation with walk-forward retraining. Stack: Python, PyTorch, CCXT, Docker, TimescaleDB, Ray Serve, Triton inference server.
Side-by-side real-time benchmark across OpenAI, Anthropic Claude, Google Gemini, DeepSeek, Qwen, OpenRouter, NVIDIA and Cloudflare, streaming token-by-token with live comparison charts. Tracks TTFT, total time, tokens/sec with p50/p95 analytics, model-cost ledger and per-prompt leaderboard. Edge deployment on Cloudflare Pages; Workers-backed API proxy with per-user key isolation. Next.js 14 · TypeScript · streaming-SSE · D1.
Closed-loop flow: policy check → evidence-pack retrieval → prompt control → model router → action runtime → audit ledger → policy feedback. Every call versioned, costed, support-traceable and replayable, applied to trading research & risk sign-off. Human-in-the-loop escalation for regulated decisions, tamper-evident audit trail with content-addressed evidence packs, and per-strategy cost attribution.
Distributed ML inference pipeline serving tons of predictions daily at sub-1ms latency across signal generation, sentiment scoring and anomaly detection. Shard-aware request routing, warm-pool autoscaling, ONNX-compiled models with fp16 quantisation, and cache-through Redis tiering. Multi-region active-active deployment with health-weighted failover. FastAPI · Redis · TimescaleDB · Triton.
Live portfolio monitoring with volatility targeting, drawdown kill-switches, per-strategy P&L attribution and cost-ledger reconciliation. Tick-level exposure dashboard across venues, automated breach alerts, scenario stress tests and VaR/ES by strategy with margin projection. Heatmaps for correlation drift and regime-change signals. FastAPI · React · TimescaleDB · WebSocket.
Multi-asset backtesting engine with 5+ years tick-level data, transaction-cost modelling, slippage curves and walk-forward optimisation. Parallel parameter-sweeps across Ray workers, regime-segmented attribution, Monte-Carlo bootstrap on trade returns and overfit-aware model selection. Deterministic replays and hash-pinned data snapshots for audit. Python · QuantConnect · Ray · Parquet.
LSTM · GRU · XGBoost stacked ensemble over 50+ technical indicators plus on-chain features (exchange flows, gas trends, active addresses). 78% directional accuracy on out-of-sample data; confidence-gated signal release and automatic retraining on regime-drift detection. Notebook-to-production pipeline with shadow trading prior to capital allocation.
Education
Coursework: Deep Learning, NLP, Machine Learning, Neural Networks, Distributed Systems, MLOps