Engineering Team Lead / Technical Lead transitioning deeper into Machine Learning Engineering and Research Engineering.

I have 10+ years of software engineering experience across investment banking, trading technology, backend platforms, applied R&D, and production-critical systems. My current focus is deep learning, LLM interpretability, representation learning, quantitative systems, and AI applications in finance and science.

• LLM interpretability and representation engineering
• Sparse Autoencoders and activation-space analysis
• Deep learning and research engineering
• Geometric deep learning and scientific ML
• Quantitative finance systems and trading analytics

Research-style project on geometric deep learning for molecular property prediction.

The project combines:

• E(n)-Equivariant Graph Neural Networks
• topological data analysis
• persistent homology features
• FiLM conditioning
• robustness evaluation under coordinate noise

Repository: qm9-egnn-tda

• Engineering Team Lead / Technical Lead in Electronic Equities Analytics IT
• Production systems for trading analytics, pre-trade risk, trading data capture, permissioning, P&L and execution analytics
• Earlier experience in computer vision and machine learning for industrial video-stream analysis
• Deep learning coursework with projects in geometric deep learning and molecular property prediction

Python, PyTorch, NumPy, pandas, scikit-learn, Java, C++, KDB+, SQL/Oracle, distributed systems, backend engineering, ML experimentation, deep learning, graph neural networks, LLM interpretability.

Research Engineering · Machine Learning Engineering · LLM Interpretability · AI for Science · Geometric Deep Learning · Quantitative Systems · Scientific Computing