AI Engineer focused on building intelligent systems that combine machine learning, symbolic reasoning, and scalable backend architectures.
My work spans applied machine learning, neuro-symbolic AI, vector search, and automation systems, with a strong emphasis on mathematical foundations, explainability, and real-world constraints.
- Applied Machine Learning and Intelligent Systems
- Neuro-Symbolic AI and Differentiable Logic
- Vector Search, Embeddings, and Similarity Systems
- Backend Architectures and Automation Pipelines
- Mathematical Modeling and First-Principles Implementations
Languages & Runtime Python · C · TypeScript
Backend & Systems FastAPI · Node.js · Docker
Data & Storage PostgreSQL · Redis · Qdrant (Vector Databases)
AI & ML PyTorch · NumPy · Pandas · LangChain
Cloud & DevOps AWS · Git · CI/CD
A Python-based framework that integrates symbolic logic with neural networks using differentiable logic. The system allows formal rules to be injected directly into neural architectures, enabling explainability, data efficiency, and constraint-aware learning.
Technologies: Python, Neural Networks, Symbolic AI, Neuro-Symbolic AI, FOL
Implementation of neural networks entirely from scratch in C, focusing on mathematical correctness, memory management, and low-level optimization.
Technologies: C, Neural Networks, Deep Learning
Implementation of a vector-based similarity search system using cosine similarity, illustrating the foundations of semantic search and retrieval mechanisms.
Technologies: Python, Vector Embeddings, Similarity Search
Physics-based simulation modeling wave interference patterns with numerical methods and interactive visualization.
Technologies: Python, NumPy, Matplotlib, Physics
Simulation of charged particle dynamics under electromagnetic fields with interactive visualization and real-time exploration.
Technologies: Python, NumPy, Plotly, Streamlit, Physics
- Preference for first-principles implementations when appropriate
- Strong focus on system design, architectural trade-offs, and explainability
- Experience building data pipelines, ML workflows, and backend services
- AI treated as core infrastructure, not as a black box