AI Engineer at PSP Investments (~$300B CAD AUM). I lead development of the Virtual Analyst Platform: agentic AI for 100+ analysts across all asset classes, cutting research cycles ~10× and powering every AI workflow the team ships.
I started in Computer Science, moved into Finance & Business Analytics at McGill, and found my edge where the two converge. I joined PSP Investments (first in information security, then digital innovation) where I quickly moved from evaluating alternative data and cyber risk to actually building the AI systems that investment teams depend on. The team evolved from Digital Innovation into AlphaScience, and I converted to full-time AI Engineer in 2023.
Today I'm leading the development of the Virtual Analyst Platform, PSP's agentic AI system in production, used by 100+ analysts across all asset classes. I designed the entire stack from scratch: a custom dual-panel UI with full agentic transparency and source traceability, a workflow launcher for named structured analyses, and the underlying AlphaScience SDK that powers every AI workflow, RAG index, and dashboard the team ships.
Outside of work, I'm a builder. I'm developing digithings.ai, a modular agentic AI toolkit that distils what I've learned at PSP into reusable components anyone can use. Having brought the flagship platform to production and scale, I'm relocating to Europe and seeking new challenges, leveraging my experience of shipping apps in production at scale with other teams across new domains and industries. I want to keep building things that genuinely matter.
PSP's flagship agentic AI platform (300 total users, 100 active & recurring), embedded in analyst–PM daily workflows across equities, fixed income, real assets, and private equity. AI findings flow directly to PMs who audit & red-team theses in the same session.
A proprietary Python SDK that is the foundation for every AI deliverable the AlphaScience team ships: every LLM workflow, RAG index, and analytical dashboard runs on it.
An ML pipeline predicting the beat/miss likelihood of earnings releases for covered companies, with a full client-facing Power BI dashboard delivering daily-updated predictions.
I'm an AI engineer with a background in CS and finance. I've spent the past 5 years at PSP Investments building AI in production for institutional investment teams, leading the agentic AI platform as it grew from POC to production. Full stack ownership: SDK, UI, onboarding, and roadmap. Now I'm looking to expand into new domains and keep building systems that deliver real value.
I came at it from both sides. CS gave me the engineering foundation; finance gave me the domain context to understand what analysts actually need. That mix has been useful for building tools that people trust and use day to day.
Source traceability. Analysts can't take an AI's word for something. I built a system that links every claim back to its exact document, page, and passage, surfaced in the UI so they can verify without leaving the platform. The engineering was one part; the other was realizing that without that trust layer, the tool wouldn't get used at all.
Structured data integration. Financial datasets are too large for context windows. I built a backend that enables the model to reference tables by path, work with compact summaries, and run ML analysis on top of that layer. Results flow back through the LLM. The same pain points exist in healthcare, legal, and other domains with large structured data.
I'm passionate about building AI systems in production that matter, and I want to keep learning and growing my expertise in the field. I'm open to any form of remote work; what matters to me is the work itself, the team, the value we ship, and the impact it has on clients.
Fully modular multi-agent AI toolkit. LangGraph orchestration, LiteLLM universal LLM wrapper, pluggable RAG backends, built-in evals, Docker deployment, MCP & Open WebUI integration, OpenClaw layer for autonomous agent deployment. Direction: “hedge fund in a box”: merging agentic AI with quantitative finance.