Benjamin Olenick

Python and applied AI engineer. I build retrieval systems, automation, and security tools, and I test what happens when language models are attacked.

8 years at BMO, Ritchie Bros., and Rexig RealtyHumber College AI & ML graduate certificateHamilton, ON · remote or Ontario hybrid

Projects

Retrieval / search

Chemister

Search and question answering over 200,000+ chemistry papers.

Problem
Chemistry papers are hard to search by meaning. Keyword search misses papers that describe the same reaction in different words, and there is no simple way to search by molecule structure.
What I built
An ingestion pipeline, bge-m3 embeddings, a FAISS vector index, hybrid keyword and vector ranking, and a FastAPI service. Answers cite the papers they come from. A molecule editor lets you search by structure.
Result
One service that searches the whole corpus in seconds and returns cited answers. Code is private; a walkthrough is available on request.
Application platform

Lumen

A home server that builds and serves apps to a thin phone client.

Problem
Phone apps are slow to change. I wanted to describe a small tool and have it on my phone the same day, without an app store release.
What I built
An async FastAPI hub that serves apps to a minimal Android WebView shell, with WebSocket push, WebAuthn login, versioned file writes, and an LLM-assisted build step that writes new apps.
Result
33 apps served from one hub. New apps and fixes reach the phone during a session with no reinstall.
AI security testing

LLM Attack Catalogue

31 documented ways to attack an LLM application, written so software can plan tests from them.

Problem
Prompt-injection advice is usually a list of example prompts. That does not tell a tester when an attack applies, or what it proves when it works.
What I built
A catalogue of 31 techniques. Each one is mapped to OWASP Top 10 for LLM Applications and MITRE ATLAS, has a write-up, and states its preconditions and results in a format an automated test planner can read.
Result
Coverage from direct and indirect injection through agent memory poisoning and model output reaching SQL, shell, and SSRF sinks. A small probe suite runs the basic cases against any chat endpoint.
Human-approved security testing

BS2

An AI-assisted testing engine where every action on a target needs an exact human approval.

Problem
AI agents that run security tools can act outside their scope, and their reports can claim things the evidence does not show.
What I built
The model proposes; a separate gate executes. Every request needs an approval tied to the exact command, destination, and policy, and it expires. Scope is rechecked before each action. Unsupported commands are refused.
Result
Open source. The 0.2.1 release passes 271 regression tests, and findings come from what actually happened, never from the model's confidence.
Security monitoring

Security Shallots

Security monitoring for a home lab or small office, on one small box.

Security Shallots screenshot
Problem
Small networks get the same attacks as big ones but cannot run a full SIEM stack or staff a security team.
What I built
Collection of network, host, and router signals, threat-intelligence lookups, cross-event correlation, and a local language model that turns alert clusters into plain-English incidents with a runbook.
Result
Known noise is suppressed automatically, the rest is ranked, and every finding keeps its source evidence. Runs on a Raspberry Pi class machine.

More on GitHub, including proximity-key (unlock a Linux machine with your phone nearby) and Selecta (a local AI music player).

Experience

Independent AI engineering projects · Hamilton, ON
Apr 2026 - Present
Graduate Certificate, AI & Machine Learning · Humber College
Sep 2025 - Apr 2026
Manager of IT (hands-on) · Rexig Realty
Jan 2023 - Sep 2025
Senior Python / Automation Engineer · Ritchie Bros.
Mar 2021 - Feb 2023
Cyber / Automation Analyst · BMO Financial Group
Oct 2018 - Oct 2020

Contact

ben.olenick@gmail.com · LinkedIn · GitHub