My expertise lies at the intersection of deep theoretical inquiry (Philosophy of Mind, Cognitive Science) and robust, scalable engineering practice. I specialize in transitioning high-level conceptual goals—such as building truly adaptive or self-improving AI—into practical, production-grade systems. I design AI pipelines that are not only intelligent but are also secure, efficient, and compliant, particularly within regulated sectors like healthcare.
🎯 I build AI that doesn't just compute, but adapts and reasons. My focus is on the robust system that enables the intelligence.
This is the technical engine room of my profile. I have organized skills by functional area to make it immediately clear to a recruiter what I can do.
- Languages: Go (GoLang), Python, C (for optimized computation)
- DevOps: Docker, Kubernetes, CI/CD Pipelines, Distributed Systems Design
- Design: Microservices, RESTful API Design, System Observability, High-Throughput Data Flow
- Frameworks: PyTorch, Hugging Face Transformers, Scikit-learn
- Modeling: LLM Fine-Tuning, RAG Implementation, NLP, Reinforcement Learning (RLHF)
- Efficiency: Model Quantization, Pruning, Low-Latency Inference, Edge Computing Optimization
- Domain: Data Privacy (HIPAA, GDPR principles), Model Interpretability (Explainable AI - XAI)
- Practice: Auditing, Secure Deployment, Ethical AI Principles, Trustworthiness Engineering
These projects demonstrate my ability to solve complex, real-world technical challenges using advanced AI methodologies.
A case study in performance engineering and data privacy.
Description: Developed a secure, localized NLP inference engine designed to run on constrained edge hardware (e.g., Jetson Orin). This project focuses on deploying powerful LLMs without requiring constant cloud connectivity, solving critical data privacy and latency issues inherent in large models.
- Key Technical Contributions: Implementing model quantization and pruning to reduce memory footprint. Designing a robust API layer for streamlined input/output on limited hardware.
- Skills Demonstrated: Edge Computing, Model Compression, Low-Latency Systems, PyTorch.
An investigation into creating self-correcting and aligned AI agents.
Description: Designed and tested novel reinforcement learning pipelines centered around Human Feedback (RLHF). The goal was to move beyond simple predictive tasks and build systems that iteratively align themselves with complex, evolving human values and functional requirements.
- Key Technical Contributions: Orchestrating complex training loops. Developing feedback mechanisms to guide model optimization. Focusing on system robustness and long-term behavioral stability.
- Skills Demonstrated: AI Alignment, Reinforcement Learning, Iterative Optimization, Fine-Tuning.
A large-scale simulation of societal or ecological dynamics.
Description: Constructed a simulation platform to model how complex, interconnected systems (like social networks or biological ecosystems) evolve based on localized feedback loops. This required designing scalable data structures and high-performance backend processing.
- Key Technical Contributions: Implementing a distributed simulation architecture (e.g., using Go concurrency). Developing predictive models to forecast system state changes based on initial parameters.
- Skills Demonstrated: Distributed Systems, Data Modeling, High-Performance Computing, Simulation Design.
My pursuit of technical excellence is driven by a fundamental interest in the nature of intelligence itself. I view the complexity of consciousness not as an abstract puzzle, but as the ultimate system design challenge.
- My View: Can we design the foundational architecture of intelligence?
- My Approach: By systematically addressing the constraints of performance, privacy, and alignment, I strive to build systems that exhibit the emergent properties we associate with advanced cognition.
I am currently seeking roles where I can lead the technical design and deployment of cutting-edge, high-integrity AI systems.
