Trust & Safety · Data · AI Automation
I build careful, trustworthy software, systems that keep people safe, turn messy data into decisions, and let machines handle the tedious work.
Every project here began as something I didn't yet know how to do. I'm a developer who's happiest at the edge of my own understanding, turning curiosity into software that earns trust, makes data legible, and automates the tedious parts away. Three questions pull me forward: Is it safe? Is it true? Can a machine do it for us?
Moderation, fraud detection, and abuse prevention: building the guardrails that let good communities scale.
Pipelines, dashboards, and geospatial insight that turn raw events into the handful of numbers teams actually act on.
Agentic workflows and document intelligence that put LLMs to work on the repetitive tasks, with humans in the loop.
Enterprise Trust & Safety operations at global scale, paired with the technical skills to build the tools that make that work faster and more consistent.
Six years of enterprise Trust & Safety operations at global scale, combining high-volume video moderation with data-driven quality analysis: auditing decision accuracy, tracking error trends, and using Python and SQL to turn large-scale review data into consistent, measurable outcomes.
ISGA Institut Supérieur d'Ingénierie & des Affaires
Oct 2019 – May 2022SUPINFO International University
Oct 2015 – May 2018A rotating set of independent side projects across trust & safety, analytics, and automation. Repos and case studies land as each ships.
A multi-modal moderation pipeline that scores text, images, and links against configurable policy taxonomies. Fast heuristic filters catch the obvious cases; an LLM triage layer handles the grey area and escalates borderline content to a human-review queue. Ships with audit logs, appeals, and per-policy dashboards so decisions stay explainable.
End-to-end event analytics: an ingestion pipeline streams product events into a columnar warehouse, and a dashboard turns them into funnels, retention cohorts, and anomaly alerts. Built to answer the question every team keeps re-asking, what changed, and why, without a data-engineering detour each time.
A framework for chaining LLM agents into reliable, repeatable workflows like ticket triage, report generation, and data enrichment. It handles tool-calling, retries, guardrails, and human-approval gates, so automations stay trustworthy in production instead of quietly going off the rails.