AI Context Summary
Profile
Name: Maciej Franciszek Matuszewski
Location: Warsaw, Poland
GitHub: github.com/mfmatusz
LinkedIn: linkedin.com/in/maciej-franciszek-matuszewski
Professional Summary
Cloud Engineer and AI enthusiast working at the intersection of Artificial Intelligence, Cybersecurity, and Cloud Infrastructure. Currently pursuing M.Sc. in AI at Warsaw University of Technology (started Feb 2026) while designing production cloud architectures at BSH Hausgeräte GmbH. Completed B.Sc. in Cybersecurity Engineering (Oct 2022 - Feb 2026) with strong technical foundation in network security, threat detection, and infrastructure automation. Experience designing serverless AWS architectures, operating Kubernetes workloads, building CI/CD pipelines, and developing intelligent security solutions.
Education & Experience
M.Sc. in Artificial Intelligence — Warsaw University of Technology
Feb 2026 - Present | Faculty of Electronics and Information Technology
Focus: Machine learning, deep learning, computer vision, AI systems engineering. Research interests: Anomaly detection, adversarial ML, intelligent automation.
Cloud Engineer — BSH Hausgeräte GmbH
Aug 2025 - Present | Warsaw, Poland
Designing and operating cloud infrastructure for global home-appliance manufacturer. Architected AWS serverless workflows (Lambda, Batch, S3, RDS), built and operate Kubernetes workloads and managed hosting platform, maintained CI/CD pipelines with GitHub Actions, developed automated data pipelines and audit systems.
DevOps Intern — BSH Hausgeräte GmbH
May 2025 - Aug 2025 | Warsaw, Poland
Implemented AWS cloud architectures using infrastructure stacks, automated cloud processes and migrated workloads to serverless architectures.
ICT Intern — PGE Dystrybucja S.A.
Jul 2024 - Oct 2024 | Lublin, Poland
Gained hands-on exposure to enterprise ICT operations in critical infrastructure environment.
B.Sc. in Cybersecurity Engineering — Warsaw University of Technology
Oct 2022 - Feb 2026 | Faculty of Electronics and Information Technology
Covered network security, cryptography, threat analysis, penetration testing and cloud-native security design. Completed projects spanning ML-based threat detection, network monitoring and infrastructure-as-code.
Core Expertise
- Cloud & DevOps: AWS (Lambda, Batch, S3, RDS), Kubernetes, Azure, Terraform, Docker/Docker Compose, Infrastructure-as-Code (IaC), GitHub Actions, CI/CD, Managed Hosting, SAM
- AI/ML: Machine Learning (scikit-learn, Random Forest, NFStreamer), Reinforcement Learning (Q-learning, Bellman equations), Deep Learning, Computer Vision, AI Systems Engineering
- Cybersecurity: Network Security, Vulnerability Scanning (Nmap, OpenVAS), Intrusion Detection (Suricata), Threat Detection, Sigma Rules, SIEM (rsyslog, Graylog, Wazuh), Penetration Testing, Malware Analysis, CTF
- Programming: Python, Go, C#/.NET, Java, Bash scripting, SQL, R, MATLAB, LaTeX, Typst
- Networking: iptables, MikroTik RouterOS, DNS (BIND9), Network segmentation, Zero-trust architecture, Site-to-Site VPN (strongSwan IPSec)
Featured Projects
Network Traffic Analysis & Threat Detection
Python, NFStreamer, scikit-learn, Sigma Rules, AbuseIPDB
Hybrid network security platform combining rule-based detection, Sigma rules engine, and Random Forest ML classifier. Detects 8+ threat types (DDoS, SYN floods, port scanning). Features IP reputation checking, geolocation mapping, and achieved 97.3% accuracy in testing. Supports both PCAP analysis and live traffic capture.
Private Network Reporter
Go, Nmap, MikroTik RouterOS, SQLite, Docker, SMTP, SHA-512 HMAC
Autonomous LAN security monitoring system for home and small-office networks. Three independent discovery sources (DHCP webhooks, Netwatch state changes, ARP table harvesting), concurrent worker pool (2-16) with dual-level per-MAC cooldown. Configurable scan depth (basic 60s, deep 300s with NSE scripts, deepsafe). SHA-512 HMAC webhook authentication with timestamp validation and replay protection. Scheduled HTML reports with NSE sensitive-script detection delivered via SMTP. Single stateless binary with embedded SQLite (WAL mode), auto-generated ECDSA certificates with TLS 1.2+. Engineering thesis project demonstrating webhook-based RouterOS integration and automated threat reporting.
Office as Infrastructure as Code
Docker Compose, iptables, strongSwan IPSec, Suricata IDS, pfSense, Graylog, Wazuh
Production-grade infrastructure-as-code implementation with 7 security zones (DMZ, Management, Backend, Trusted, Guest, WAN, Honeynet), 6 iptables firewalls enforcing zero-trust policies, IPSec IKEv2 site-to-site VPN (aes256-sha384-modp2048). 23-container stack including dual Suricata NIDS, SIEM (rsyslog, Graylog), EDR (Wazuh), vulnerability scanning (Tenable Nessus), internal services (GitLab, Nextcloud, pfSense). 20+ automated E2E tests validating firewall rules, network segmentation, service availability. Full documentation in Typst. Demonstrates enterprise network security principles in reproducible IaC format.
THE EYE
Backend Development, Distributed Systems, Computer Vision, Edge Computing, REST API
Distributed intelligence platform transforming optical sensors into defense-grade threat detection nodes for airspace security. Addresses unmanned aerial threats (responsible for 80% of casualties in modern conflicts per NYT analysis). Backend infrastructure: high-throughput sensor data ingestion, real-time threat classification with computer vision and ML, distributed processing coordination across edge devices, robust REST API for command dashboard, secure communication protocols. Sub-second detection latency (<1s), 24/7 operation capability, $50+ per-device deployment costs with OTA updates. Suitable for critical infrastructure protection, military installations, public event security, and border monitoring.
OpenVAS Automation Wrapper
Python, OpenVAS/GVM, python-gvm, netifaces, SMTP, pytest
Automation layer transforming OpenVAS into cron-schedulable security monitoring system. Zero-config subnet detection using netifaces (scans local networks automatically), idempotent GVM target creation (creates or reuses existing targets), automated Full and Fast scan execution, PDF report extraction and SMTP delivery. Includes comprehensive pytest test suite with mocked GVM/SMTP for CI/CD integration. Streamlines vulnerability management workflow for continuous security monitoring without manual intervention. Perfect for scheduled background scans in home labs and SOC environments.
Plague: Advanced Malware Research
Python, C#/.NET, Paramiko, Vagrant, VirtualBox, GitLab CI
Academic malware research platform demonstrating complete attack kill chain. Adaptive intelligence with 5 operational modes, multi-protocol lateral movement (SSH, SMB, FTP, HTTP), self-replication with timestomping, AES-256 ransomware payload. Deployed in isolated Vagrant lab with intentionally vulnerable VMs. FOR EDUCATIONAL AND DEFENSIVE RESEARCH ONLY.
Q-Learning MountainCar
Python 3.10+, Gymnasium, NumPy, pytest
Reinforcement learning implementation solving MountainCar-v0 control problem. Tabular Q-learning with Bellman equation (Q(s,a) ← Q(s,a) + α·[r + γ·max Q(s',a') - Q(s,a)]), state discretization (continuous → 20×20 grid), epsilon-greedy exploration with linear decay. Discovers counter-intuitive momentum strategy autonomously.
Availability
Open to discussing interesting opportunities in AI, cloud architecture, or security challenges for select projects. Available for consulting or B2B-freelance work on compelling problems that align with expertise in machine learning, infrastructure security, or DevOps.
Key Technologies
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