CV
Education
- M.S. in Information Technology – Information Security — Carnegie Mellon University, Pittsburgh, PA
Jan 2023 – Jan 2025 | GPA: 3.93 - M.S. in Computer Engineering — University of Patras, Patras, GR
Oct 2021 – Jun 2022 | GPA: 3.63 - B.S. in Computer Engineering — University of Patras, Patras, GR
Oct 2017 – Aug 2021 | GPA: 3.30
Experience
- AI/ML Software Engineer — Pervaziv AI, Sunnyvale, CA
Feb 2025 – Present- Built an agentic IDE extension using Google ADK and MCP connectors for secure code generation workflows (100+ users)
- Contributed to Meta’s PurpleLlama project, enabling secure code benchmarking for fine-tuned LLMs including Gemini
- Optimized LLMs using RAG techniques and external knowledge bases, improving code understanding accuracy by 27%
- Machine Learning Engineer Intern — Pervaziv AI, Mountain View, CA
May 2024 – Aug 2024- Designed, implemented, and deployed SecOps features using fine-tuned Gemini and OpenAI models for NLP and GenAI tasks
- Increased ML-based vulnerability detection rate by 430% through advanced feature engineering and model optimization
- Reduced ML inference time by 65% for AI-driven code remediation by optimizing data pipelines and model deployment
- Machine Learning Researcher — University of Patras, Patras, GR
Feb 2020 – Jun 2022- Published 3 peer-reviewed papers on applying machine learning to optimize 5G network performance and resource allocation
- Authored a 40-page book chapter on emerging ML techniques used in 5G Networks, focusing on data-driven optimization
- Presented findings at 3 accredited 5G international conferences
Projects
- ML-Based Fraud Detection System with Agentic Analysis — Python, Google ADK, AWS
Oct 2025 – Jan 2026- Developed an ML-based fraud detection system using XGBoost for real-time transaction classification with 99% accuracy
- Implemented AI agents for temporal pattern analysis, enabling cross-transaction correlation for enhanced fraud detection
- Practicum Project – AdBlocker Data Science — Python, Apache Spark, Pandas
Aug 2024 – Dec 2024- Led the analysis of 1TB+ of user interaction data using Apache Spark, building data loaders for large-scale ML experiments
- Developed and benchmarked ML models in PyTorch and TensorFlow, focusing on experiment design and evaluation metrics
- Leveraging Generative AI for Log Analysis — Python, LangChain, Agentic AI
Mar 2024 – Apr 2024- Developed a LangChain-based agent to process and analyze structured and unstructured log data for forensic analysis
- Built a Streamlit UI for interactive data exploration and annotation
- GNN for C2 Channel Detection — Python, PyTorch, GNNs
Jan 2024 – Apr 2024- Built and deployed a Graph Convolutional Neural Network for sequence and pattern detection, achieving 90.3% accuracy
- Implemented in PyTorch using GCNConv with experiment tracking and performance analysis
- ML Techniques for Resource Allocation in 5G Networks — Python, Pandas, TensorFlow
Oct 2021 – Jun 2022- Developed Deep Reinforcement Learning algorithms for optimal resource allocation in simulated 5G networks
- Achieved 300% throughput increase, 150% QoS improvement, and 50% energy reduction using Deep Q-Learning
