Athanasios Masouris

AI Researcher

Resume

About Me

As an AI Researcher at Shell in Amsterdam, I design and implement advanced generative AI and multi-agent system solutions to tackle complex business challenges. My work spans from exploring state-of-the-art AI methodologies to delivering scalable, production-ready systems that enhance decision-making and operational efficiency.


Prior to joining Shell, I worked as an AI Engineer at PwC Greece, where I developed virtual assistants, AI agents, and end-to-end machine learning pipelines using Azure Machine Learning and Databricks. Following MLOps best practices, I delivered AI solutions that were both scalable and reliable for diverse business needs.


I hold an MSc in Computer Science (with distinction) from TU Delft, specializing in Artificial Intelligence with published research in computer vision and end-to-end recognition systems. I also earned a joint BSc & MSc in Electrical and Computer Engineering from NTUA, focusing on AI, deep learning, and GAN-based generative modeling. In addition, I am certified as a Microsoft Azure AI Engineer Associate and Data Scientist Associate, along with other industry-recognized credentials in AI, cloud, and MLOps.


I am passionate about bridging the gap between cutting-edge AI research and practical business applications. My hands-on approach ensures that complex technical solutions are robust, scalable, and aligned with strategic objectives. I enjoy collaborating with cross-functional teams to translate real-world challenges into impactful AI-driven products.

Professional Experience

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AI Researcher

Shell
Amsterdam, the Netherlands
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  • Developing multi-agent systems: Designing and implementing collaborative AI agents to address complex business challenges
  • Applying GenAI research: Translating emerging research in generative and multi-modal AI into practical, high-impact solutions
  • Innovating with novel architectures: Experimenting with new AI models and workflows to enhance decision-making and automation
Generative AI Multi-Agent Systems Research Python
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AI Engineer

PwC
Athens, Greece
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  • Developed generative AI solutions: Designed and implemented retrieval-augmented generation (RAG) virtual assistants in Python and C#, enhancing domain-specific query resolution and user engagement
  • Built end-to-end ML pipelines: Created comprehensive machine learning workflows—from data ingestion and feature engineering to model training and deployment - using Azure Machine Learning
  • Developed scalable backend systems: Engineered robust APIs using Django Rest Framework to support high-impact analytics applications in the public sector
  • Streamlined deployment workflows: Leveraged containerization and Azure cloud services to deliver scalable, reliable AI solutions
  • Collaborated across teams: Worked closely with cross-functional stakeholders to translate business requirements into technical deliverables, contributing to the continuous improvement of development practices and tooling
Artificial Intelligence (AI) Generative AI Machine Learning Microsoft Azure Cross-functional Collaborations Backend Development Python Django Rest Framework C# ASP.NET Core Qdrant Scikit-learn Databricks AzureML
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Military Service

Hellenic Army
Greece
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  • Digitized archived documents
  • Developed scripts to automate the generation of patrol schedules
Python MS EXcel VBA

Internships

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Contributor

Google Summer of Code @ Intel OpenVINO Toolkit
Remote
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Project: Train a DL model for synthetic data generation for model optimization

Python PyTorch OpenVINO Flask API HTML JS Deep Learning Computer Vision Generative Adversarial Networks (GANs) Knowledge Distillation Model Quantization LaTeX
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Machine Learning Intern

NCSR DEMOKRITOS
Athens, Greece
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Project: Automatic Video-Game Review Summarization

  • Refactored and rewrote the entire codebase of the pipeline for the review summarization system in Python
  • Evaluated machine learning classifiers for identifying the reviewed aspects of the video-game in users' reviews
  • Proposed a meta-classification approach which improved the overall performance of the pipeline
Python PyTorch Scikit-learn Natural Language Processing (NLP) Classification Embeddings

Projects

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FlexiBOT - Cloud-Agnostic RAG Chatbot
FlexiBOT is a cloud-neutral chatbot built with ASP.NET Core, using retrieval-augmented generation (RAG). It features a semantic cache for quick response retrieval, integrates open-source models via Ollama, and uses RabbitMQ for event-driven processes. The system employs modular workers for document handling, with MinIO for storage, Qdrant for vector searches, PostgreSQL for data management, and Redis for caching conversation history, providing an efficient and scalable solution for conversational AI. View more..
C# ASP.NET Core RabbitMQ Ollama Qdrant Redis PostgreSQL MinIO HTML/CSS/JS Retrieval Augmented Generation (RAG) Hypothetical Document Embeddings (HyDE)
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RAGPal: A PoC RAG-based virtual assistant
RAGPal is implemented as a web application with a front-end interface for user interaction and a FastAPI-based back-end for handling requests and business logic. The system utilizes Azure OpenAI API resources for chat completion and embedding generation, and the Qdrant vector database to serve as the knowledge base for storing and retrieving documents. View more..
Python FastAPI AzureOpenAI API Qdrant HTML/CSS JavaScript Large Language Models (LLMs) Retrieval Augmented Generation (RAG) Prompt Engineering
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End-to-End Chess Recognition
The goal of this project was to develop a methodology that would predict the chessboard configuration in an input image of a chessboard. Contrary to the predominant approaches, that aim to solve this task through the pipeline of chessboard detection, square localization, and piece classification, we relied on the power of deep learning models to directly predict the configuration from the entire image. View more..
Python PyTorch Lightning Deep Learning Computer Vision Image Processing
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Chess Recognition Dataset (ChessReD)
The Chess Recognition Dataset (ChessReD) is a comprehensive collection of images of chess formations that were captured using various smartphone cameras. It comprises 10,800 images from 100 chess games. The dataset features a wide range of chess piece configurations, captured under different angles and lighting conditions. The dataset includes detailed annotations about the pieces' formations in chess algebraic notation, providing valuable information for chess recognition research. View more..
Deep Learning Computer Vision Chess Recognition Object Detection
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TeleGAN: Text-To-Image Synthesis using GANs
The goal of this project was to develop a novel architecture which would be able to generate high-resolution images conditioned on a given text description. The proposed model (TeleGAN) decomposes the difficult task of high-quality image generation, into the more manageable sub-problems of low-res black-and-white image generation, colorization, and resolution enhancement, in three consecutive stages. View more..
Python PyTorch Deep Learning Computer Vision Conditional Image Generation Text-to-Image Generative Adversarial Networks (GANs)
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Other projects
The rest of my projects can be found on my GitHub account.

Education

Delft University of Technology

MSc in Computer Science
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  • Track: Artificial Intelligence
  • Grade: 8.5/10 (Cum Laude)
  • Thesis: End-to-End Chess Recognition
TU Delft

National Technical University of Athens

Joint BSc & MSc in Electrical and Computer Engineering
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  • Concentration field: Computer Science
  • Grade: 8.27/10
  • Member of the Artificial Intelligence and Learning Systems Laboratory (AILS lab)
  • Thesis: Text-to-image synthesis using Generative Adversarial Networks (GANs)
NTUA

Certifications

  • Azure Administrator Associate (July 2025) by Microsoft [credential]
  • Azure Data Scientist Associate (May 2025) by Microsoft [credential]
  • Azure Data Fundamentals (March 2025) by Microsoft [credential]
  • Azure AI Engineer Associate (Dec 2024) by Microsoft [credential]
  • Machine Learning Engineering for Production (MLOPs) (Nov. 2023) by deeplearning.ai [credential]
  • Azure AI Fundamentals (May 2023) by Microsoft [credential]
  • Azure Fundamentals (May 2023) by Microsoft [credential]
  • Deep Learning Specialization (Aug. 2019) by deeplearning.ai [credential]
  • Machine Learning (Apr. 2019) by Stanford|Online [credential]

Publications

  1. Athanasios Masouris and Jan van Gemert (2024). End-to-End Chess Recognition. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, pages 393-403
    [SciTePress] [arXiv]

Skills

Programming/Scripting Languages
  • Python
  • C#
  • SQL
  • JavaScript
  • HTML/CSS

Machine Learning and Artificial Intelligence
  • AzureML
  • Databricks
  • Scikit-learn
  • GenAI
  • LangGraph
  • MCP
  • HuggingFace
  • Ollama
  • Agentic AI
  • PyTorch

Cloud and DevOps
  • Microsoft Azure
  • Docker
  • CI/CD
  • Databricks Asset Bundles
  • Git
  • Devcontainers

APIs/Backend
  • Django Rest Framework
  • FastAPI
  • ASP.NET Core

Professional
  • Cross-functional collaboration
  • Stakeholder management
  • Requirements analysis
  • Project delivery

Contact