IT

Adem Bilge

Software Engineer, Data Scientist, AI Engineer, Applied Researcher

I build intelligent applications by combining machine learning and AI agents with experience in backend engineering and distributed systems.

Based in Milan, Italy
Studying at Politecnico di Milano

Firat Adem Bilge
Engineering with curiosity. Building with care.

AI engineering, intelligent systems & applied ML

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Complex systems.
Clear thinking.

I like building software that works beyond demos, with real users, real data, and real constraints.

Adem overlooking the hills and city of Turin
A little perspective, away from the terminal.

I’m Adem Bilge, a software engineer and Master’s student at Politecnico di Milano.

I’m based in Milan, where I study Computer Science and Engineering. My current focus is AI engineering: understanding how learning algorithms, large language models, and AI agents can become useful, reliable software. My studies explore advanced AI techniques, agentic systems, multiagent learning, and the engineering decisions needed to bring these ideas into working applications.

That direction builds on hands-on experience at HAVELSAN, Entervia, and ULAK. I’ve worked on distributed data processing with Dask, services on large Kubernetes clusters, real-time Kafka pipelines, and low-latency gRPC microservices. Those projects taught me to think beyond an individual feature and consider the data, infrastructure, observability, and people around it.

My machine learning experience includes a production fingerprint segmentation model and published research on low-resolution sign language recognition. Through that work, I’ve learned to connect experimentation and evaluation with practical constraints such as image quality, privacy, and deployment.

I’m looking for opportunities across software engineering, data science, applied research, and AI engineering where I can combine this foundation with my current studies. I value clear communication, careful testing, and choosing solutions that a team can understand, maintain, and improve.

Explore my experience

600+TB

Data processed with Dask

300+nodes

Kubernetes in production

92%

Fingerprint model accuracy

Selected work

From knowledge retrieval to encrypted messaging. A few things I’ve built to explore how software can work better.

Cognify knowledge vault dashboard

Cognify

Personal knowledge, made searchable.

A second brain that reads your documents. An asynchronous pipeline extracts, summarizes, classifies, and embeds files, with semantic search and cited answers across your vault.

Django · Next.js · Celery · Transformers

Inside Cognify

Document intelligence

Cognify is a full-stack personal knowledge management system — a second brain that reads your documents. Scanned PDFs, images, and plain-text files enter an asynchronous Celery worker pipeline.

OpenCV preprocessing and Tesseract OCR extract text. DistilBART summarizes it, a zero-shot MNLI classifier tags topics, and Sentence-BERT creates semantic embeddings for search and analysis.

A workspace for knowledge

The Next.js 16 dashboard brings together a knowledge vault, Recharts analytics, and browser-native PDF tools to split, merge, and reorder documents.

Document versioning preserves parent-child lineage. Sensitive mode, soft-delete recovery, collections, annotations, bookmarks, and notes keep documents and quotations organized.

Retrieval & answers

Cosine similarity search finds meaning across documents rather than relying only on keyword matches. In the global RAG chat, RoBERTa generates cited answers from the most relevant chunks in the vault.

Upload → OCR → Summarize → Classify → Embed → Completed

Access & operations

The Django REST backend enforces JWT authentication, user-scoped data isolation on every query, and audit logging for every action.

35 automated tests, Docker Compose orchestration, and Prometheus worker-latency metrics support the implementation. MIT licensed.

Built with Python, Django, Next.js, Celery, Transformers, Sentence-BERT, Docker, Prometheus

CryptMe

A closer look at encrypted communication.

A Java client-server messaging project exploring symmetric encryption, TCP sockets, and local encryption and decryption in a Swing desktop client.

Java · TCP sockets · AES / DES · Swing

View repository
CryptMe Swing chat client with username, encryption mode, and messaging controls
The original CryptMe desktop client
Inside CryptMe

Real-time messaging

CryptMe is a client-server messaging application built entirely in Java. Users register unique usernames, connect to a central server, and join a shared chat room.

TCP sockets carry encrypted payloads between connected clients. The server manages connections, message routing, and disconnects without reading plaintext.

Encryption in the client

The Swing GUI performs encryption and decryption locally. Users can switch at runtime between AES (128-bit) with a 16-byte initialization vector and DES (56-bit) with an 8-byte IV.

Selecting a method generates a fresh secret key and IV for the session and distributes them to participants.

Implementation notes

This learning project demonstrates symmetric cryptography in a multi-user chat. Its original implementation includes DES and a persistent log of generated session keys and IVs for auditability.

Built with Java, AES, DES, Socket Programming, Swing GUI

The server in action

CryptMe server console

Built in
production

4+ years of engineering experience across distributed analytics, applied computer vision, startup services, and telecom infrastructure.

View full CV
Mar 2025 — Aug 2025

Entervia

Software Engineer

Backend services and AI agents for session booking, scheduling, and participant onboarding.

Contributions & technologies

Part-time · Early-stage startup · Milan, Italy

  • Developed backend services and RESTful APIs using Django to support session booking and participant management.
  • Managed AWS infrastructure and PostgreSQL databases with Docker and Jenkins to implement CI/CD pipelines.
  • Implemented agentic workflows with tool integration to manage session creation, scheduling, and content generation.
  • Integrated autonomous AI agents into Telegram bot services to handle real-time participant onboarding and Q&A.

Python, Django, gRPC, Postman, Swagger, Pytest, PostgreSQL, Redis, AWS, Docker, Jenkins, Git, CI/CD

Sep 2022 — Sep 2024

HAVELSAN

Software Engineer

Distributed analytics over 600+ TB of data, running across 300+ production Kubernetes nodes.

Contributions & technologies

Full-time · Defense & IT — EyeMiner video analytics platform · Ankara, Turkey

  • Developed a distributed computing platform on Linux using Dask to process analytical workloads on 600 TB+ data.
  • Operated Docker services on a Kubernetes cluster across 300+ production nodes for maintaining high availability.
  • Developed gRPC microservices and Kafka pipelines to enable low-latency and fault-tolerant service communication.
  • Built observability solutions using Prometheus, Grafana, and Elasticsearch to improve monitoring and diagnostics.

Python, Dask, Pandas, gRPC, Unittest, PostgreSQL, Elasticsearch, Kubernetes, Docker, Kafka, Helm, Prometheus, Grafana, Linux, Jira, Agile/Scrum

Jun 2021 — Aug 2022

HAVELSAN

Machine Learning Engineer

A production fingerprint segmentation model, from image preprocessing to 92% accuracy.

Contributions & technologies

Full-time · AFIS fingerprint recognition system · Ankara, Turkey

  • Architected a computer vision model for fingerprint segmentation using PyTorch to support biometric identification.
  • Optimized model performance through data augmentation techniques and systematic hyperparameter tuning.
  • Built image preprocessing pipelines using OpenCV to enhance data quality for model training and evaluation.
  • Delivered a production-ready fingerprint identification solution achieving 92% accuracy with an automated workflow.

Python, PyTorch, OpenCV, Anaconda, Jupyter Notebook, Jira, Agile/Scrum

Jun 2020 — Oct 2020

ULAK HABERLEŞME

Software Engineer

Low-latency messaging services, database optimization, and cloud deployments.

Contributions & technologies

Internship · National telecom infrastructure company · Istanbul, Turkey

  • Developed backend services for a messaging platform using FastAPI and WebSockets, enabling low-latency messaging.
  • Optimized MySQL database performance through indexing and schema design to improve application responsiveness.
  • Managed AWS deployments and implemented CI/CD pipelines to support software delivery and release processes.

Java, MySQL, AWS, Docker, Jenkins, Git, Linux, Jira, Agile/Scrum, CI/CD

The whole system

My strongest work happens where infrastructure, data engineering, and applied machine learning meet.

Machine learning

Practical computer vision, from image preprocessing and model training to production deployment.

PyTorch, OpenCV, Pandas, NumPy

Backend engineering

Stable APIs and cloud-native services designed for reliability, performance, and long-term maintenance.

Python, Django, FastAPI, gRPC, PostgreSQL

Cloud & DevOps

Container orchestration, repeatable delivery, and the observability to understand what’s happening in production.

AWS, Docker, Helm, Jenkins, Prometheus, Grafana

Distributed systems

Scalable data pipelines and distributed execution that stay measurable under real production pressure.

Dask, Kafka, Kubernetes, Linux

All technologies & tools

Programming Languages

Python, Java, C++, SQL

Big Data & Machine Learning

Dask, Kafka, PyTorch, OpenCV, Pandas, NumPy, Jupyter, Anaconda

Backend & APIs

Django, FastAPI, gRPC, Postman, Swagger, Pytest, Unittest

Databases

PostgreSQL, MySQL, Redis, Elasticsearch

Cloud & DevOps

Kubernetes, Docker, AWS, Helm, Jenkins, Apache Kafka, Prometheus, Grafana

Tools & Methodologies

Git, Linux, Jira, Agile/Scrum, CI/CD

Turkish Native

English C1 · Professional

Italian B1 · Intermediate

Always learning

A foundation in computer science. A continuing interest in how research becomes useful software.

Sep 2024 — Present · Milan, Italy

Politecnico di Milano

Master of Science - Computer Science and Engineering

Master's program

Study & coursework

My Master's studies focus on artificial intelligence, AI agents, and multiagent systems. I explore how advanced learning methods, large language models, and collaborating agents can become reliable, practical software through AI-enabled systems engineering.

Coursework: Advanced AI Techniques, Natural Language Processing, Engineering AI-Enabled Software Systems, Multi Agent Learning, From LLMs to Agentic AI, Multiagent Systems.

Oct 2017 — May 2022 · Ankara, Turkey

Hacettepe University

Bachelor of Science - Computer Science

GPA 3.33/4 · Honor Student

Study & coursework

During my bachelor's, I focused mainly on Machine Learning, Computer Vision, and Software Engineering. My thesis work in computer vision later became my publication Tinysign (SIViP, 2024), and that process really strengthened how I approach programming, algorithms, and real problem-solving in practical engineering contexts and team delivery.

Coursework: Algorithms and Data Structures, Software Engineering, Databases, Deep Learning, Machine Learning, Computer Vision.

Sep 2019 — Feb 2020 · Czestochowa, Poland

Czestochowa University of Technology

Erasmus+ Exchange Program

International exchange semester

Study & coursework

I joined this Erasmus semester to improve my background in AI and Computer Vision in a completely new academic environment. Working in international project teams helped me become more adaptable, collaborative, and confident in technical communication across different cultures, academic expectations, and working styles in team settings.

Coursework: Applications of Artificial Intelligence, Machine Learning, Computer Vision.

Research, applied

Computer vision for the constraints of the real world.

Tinysign research examples comparing low-resolution sign language footage and pose information
Low-resolution sign language recognition

Tinysign

Sign Language Recognition in Low-Resolution Settings

First-author research on recognizing sign language when video quality and bandwidth are limited. Published in Signal, Image and Video Processing, Springer Nature.

June 2024 · SIViP 18, 6881–6890 · London, United Kingdom

Abstract highlights & citation
  • Introduced the first benchmark for low-resolution sign language recognition.
  • Addressed recognition in privacy-sensitive and low-bandwidth video scenarios.
  • Proposed a novel pose-conditioned super-resolution architecture to reconstruct critical visual cues.
  • Achieved 58.0% accuracy, outperforming the previous state of the art (35.0%) on the same dataset.

Bilge, F.A., Hüseyinoǧlu, A., Bilge, Y.C. et al. Tinysign: sign language recognition in low resolution settings. SIViP 18, 6881–6890 (2024). DOI: 10.1007/s11760-024-03358-z

Let’s build
what’s next.

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