About
I'm an AI infrastructure engineer focused on the problem of agent memory — how AI agents remember, share, and curate knowledge across sessions and across teams of agents.
I'm the founder of Quantify Labs Ltd. (UK) and the creator of Aegis Memory, an open-source persistent memory system for multi-agent AI. I also built mcp-parapet, security middleware for the Model Context Protocol targeting EU AI Act compliance.
Before AI infrastructure, I spent 10+ years in data engineering and business intelligence, working with SQL, Python, Azure, Databricks, and enterprise data pipelines. That background shaped how I think about agent systems — reliability, observability, and failing gracefully matter more than demos that work once.
I hold an MSc in Big Data Analytics from Sheffield Hallam University and I'm building toward a funded PhD (2027–28 target) researching multi-agent memory systems and collective knowledge discovery — specifically, whether quality-curated memory can make multi-agent simulation reliable enough to trust.
What I'm working on now
Learning AI agent infrastructure systematically through an 80-lesson curriculum covering LangChain, LangGraph, CrewAI, production deployment, and observability. Each lesson becomes a blog post here — not a learning diary, but a reference that explains what I built, what I learned, and what the documentation doesn't tell you.
In parallel: running a Polymarket trading bot (paper trading phase), preparing mcp-parapet for open-source launch, and writing a three-paper research arc for PhD applications.
Certifications
- Microsoft Certified: Azure Data Engineer Associate
- Databricks Generative AI & Data Engineering
- Oracle Cloud Data Management
- DBT Fundamentals