Software engineer working at the intersection of security, data, and machine learning — drawn to systems that detect fraud, vulnerabilities, and threats. Background in programming education; now focused on cyberdefense.

Designing multi-agent systems and putting them to work — not as demos, but as pipelines that produce real output under hard verification.

• ensamble — a multi-agent SDLC orchestration framework where reliability is architectural, not prompted: the agent's output is treated as untrusted input. A fail-closed engine computes completion by re-running the project's real checks instead of believing the model, and a secret-scanning gate hard-blocks secrets in agent writes. Secure-by-default.
• monitor — a private, multipurpose Python + Rust scraper and Telegram bot running 24/7. Strict TDD (300+ tests), architecture contracts enforced in CI, and a hardened secrets path: keys in the OS keyring, fail-loud, with adversarial leak-tests asserting no token reaches logs or sinks.
• Knowledge graphs & technical writing — I design knowledge systems and the pipelines that produce them. Privately, apuntes.md is an agentic pipeline (auditor↔enricher loops, deterministic quality gates) that rebuilds my Cyberdefense coursework into curated, audited study material — theory, solved practice, and atomic notes — grounded in evidence-based pedagogy (Ausubel · Sweller · Mayer · Bloom · Matuschak). Publicly, I write long-form posts on my blog and maintain a technical atlas — a knowledge graph of security concepts as atomic, interlinked nodes, navigable as a D3 force-directed graph.

March 2023 - May 2025 · Remote · Cohorts of ~50 students with prior programming experience

Designed and delivered a 24-class hands-on curriculum across three progressive modules, taught live through interactive Google Colab notebooks where theory and exercises lived inline — authored with explicit pedagogical scaffolding (advance organizers, worked examples, retrieval prompts, error taxonomies).

• Python foundations: types and control flow, pattern matching, OOP and design patterns, error handling, modules, and file I/O — applied to ETL exercises on real-world datasets.
• Intermediate / advanced Python: generators and lazy evaluation, comprehensions, closures and decorators (timer, debug, memoize), regex-driven validation and extraction, ETL pipelines built around @dataclass and match/case.
• Data Science & AI: NumPy vectorization, REST API integration with caching, Pandas data wrangling, data visualization with Seaborn/matplotlib, SQL and SQLAlchemy (Core/ORM), applied transfer learning via Hugging Face pipelines, and a closing module on exploratory data analysis (EDA).

• B.S. in Cyberdefense — UNDEF · 2026 – 2029, in progress
• Online courses: CS50 (Harvard), The Missing Semester (MIT), Python Programming MOOC (Helsinki)