I'm an Applied AI & Data Professional with an MSc in Artificial Intelligence and Applications and 10+ years of experience across financial operations, payroll systems, analytics, automation, and data-intensive environments.

I currently serve as an Information Technology Officer (Captain), Joint IT Corps, Hellenic Armed Forces, working with large-scale payroll and HR data supporting 55,000+ personnel.

My current focus is the transition from analytics and academic AI into production-oriented AI and machine learning engineering, with particular interest in:

• Retrieval-Augmented Generation (RAG)
• Applied Machine Learning
• LLM-powered applications
• Python backend development
• SQL and data-intensive systems
• API development and containerization
• Cloud-based AI deployment

I enjoy working at the intersection of AI, data, software engineering, and real-world business problems — particularly where reliability, traceability, and structured evaluation matter.

My MSc thesis explored the design and empirical evaluation of Retrieval-Augmented Generation systems for financial-document analysis.

The system was evaluated on real financial filings using multiple retrieval architectures:

• Dense retrieval with BGE-M3 + FAISS
• Hybrid retrieval using FAISS + BM25
• Reciprocal Rank Fusion (RRF)
• Cross-encoder reranking
• Query expansion for financial terminology
• Retrieval and generation evaluation with RAGAS and RAGChecker

The work focused not only on building a RAG pipeline, but on systematically studying retrieval quality, evidence coverage, faithfulness, hallucination, and failure modes.

Tech: Python · BGE-M3 · FAISS · BM25 · RRF · Cross-Encoder Reranking · RAGAS · RAGChecker

An end-to-end machine learning project focused on customer churn prediction and decision support.

The project explores the full ML workflow:

• Data preprocessing and exploratory analysis
• Feature engineering
• Model training and comparison
• Classification evaluation
• Explainability and business interpretation
• Reproducible ML project structure

Tech: Python · pandas · scikit-learn · Machine Learning · Data Visualization

I'm currently strengthening the engineering layer around my AI and ML background:

Applied AI
├── Retrieval-Augmented Generation
├── LLM applications
├── Evaluation & grounding
└── ML systems

Software Engineering
├── Python
├── FastAPI
├── REST APIs
├── Testing
├── Git / GitHub
└── CI/CD

Infrastructure
├── Docker
├── PostgreSQL
├── Vector databases
└── Cloud deployment

Machine Learning · RAG · Embeddings · Hybrid Retrieval · Reranking · LLM Evaluation

SQL · Oracle SQL / PL/SQL · pandas · Power BI · Excel

REST APIs · Pytest · CI/CD · Pydantic

MSc — Artificial Intelligence and Applications University of Thessaly Grade: 9.65 / 10

Thesis: Design and Empirical Evaluation of Retrieval-Augmented Generation Systems for Financial Document Analysis

Diploma — Analysts–Programmers Program (ΣΠΗΥ) Hellenic Army School of Computer Programmers Grade: 96.01 / 100

Bachelor's Degree — Economics Aristotle University of Thessaloniki Grade: 8.02 / 10

• Google Data Analytics Professional Certificate
• Microsoft Power BI Desktop for Business Intelligence
• Machine Learning A-Z: AI, Python & R

During my MSc, I also worked on projects covering:

These projects provided the theoretical foundation that I am now extending through more production-oriented AI and ML engineering work.

I'm particularly interested in opportunities around:

Applied AI Engineering · Machine Learning Engineering · AI Software Engineering · Data & AI Engineering

especially roles involving:

• LLM / RAG applications
• ML-powered products
• Python backend development
• Search and retrieval
• AI evaluation
• Financial or enterprise data

Athens, Greece 🇬🇷