My Story: From Operational Clarity to Cognitive Systems
For nearly two decades, my career has been about transforming operational chaos into clarity. I’ve designed, optimized, and automated the large-scale infrastructure for systems serving millions of daily transactions, boosting system availability to 99.5% and ensuring the reliability of mission-critical services. From leading incident responses to reverse-engineering complex hardware, my core drive has always been to build tools and frameworks that make systems not just work, but behave with intelligence and resilience.
This journey naturally led me to artificial intelligence—not as a trend, but as the “missing layer” that could make the systems I build truly adaptive. I began by embedding intelligence into daily workflows, building practical tools like custom alerting pipelines that reduced repeat incidents by ~30% and GPT-based summarizers that turned fragmented logs into structured reports.
These explorations from applying AI to questioning its core limitations led to my current focus: the intersection of engineering, architecture, and responsible AI. My work now spans both research and implementation, combining system-wide foresight with hands-on execution to build the next generation of intelligent systems that aren’t just automated, they’re adaptive, traceable, and human-aligned.
My Philosophy
I believe the most impactful AI systems will be those built on a foundation of trust and transparency. My work is guided by a few core principles:
- The Two-Gear System Architect: I work in two distinct gears.
- Gear 1: Horizontal Scanning (Breadth): I scan horizontally across related domains (inside and outside of IT), tools, teams, and prior art to spot patterns, constraints, and the best starting root.
- Gear 2: Vertical Application (Depth): I choose the optimal architectural thread and take it all the way—architecture, code, tests, and rollout—applying best practices and methodologies strongly and deeply to ensure production success.
- Principled by Design: Ethical considerations like privacy, governance, and user agency should be architectural pillars, not afterthoughts. My research on auditable governance is a direct reflection of this belief.
- Clarity Through Structure: Complexity can be managed with clear, modular, and observable systems. Whether it’s a backend service or a cognitive model, I build for interpretability.
- From Research to Reality: The most powerful ideas are those that can be implemented. I thrive on connecting first-principles research directly to the engineering of robust, working systems.
- Tools Should Empower, Not Overtake: An AI assistant should be a reliable, user-controlled tool that enhances human capability. My focus on memory and personalization is always in service of this goal, with guardrails to protect user autonomy.
Skills & Technologies
I have a broad technical skill set spanning infrastructure, automation, and AI development.
Programming & Data
- Python, Bash, React, TypeScript
- Pandas, Nympy, NetworkX, Matplotlib
- SQLite
AI/ML Frameworks
- Scikit-learn, spaCy
- FastAPI, Flask
- LangChain & LlamaIndex
- GPT & Transformers APIs
Infrastructure & MLOps
- Linux, Docker, Ansible
- Git, Zabbix, Grafana
- CI/CD (GitHub Actions)
Research & Documentation
- LaTeX
- Markdown
- PlantUML