Firat Adem Bilge

Mid Level Software Engineer

I build distributed systems that work at real scale: a production big-data platform processing 600+ TB with Dask on a 300+ node Kubernetes cluster, real-time streaming with Kafka, and ML models shipped to production environments at global scale across diverse industry domains. Author of a peer-reviewed computer vision paper (SIViP, 2024).

Portrait of Firat Adem Bilge
600 TB+
Data processed with Dask
300+
Kubernetes nodes in production
92%
CV model accuracy in production
4+ yrs
Engineering experience
01 — About

A Bit About Me

I am a software engineer and Master's student in Computer Science and Engineering at Politecnico di Milano. I like building software that works beyond demos and handles real users, data, and scale.

My background sits between backend engineering, cloud infrastructure, big data, and machine learning. I have worked on distributed data processing with Dask, containerized microservices on large Kubernetes clusters, real-time streaming with Kafka, and low-latency services with gRPC. I also spent time on computer vision projects, including a production fingerprint segmentation model and a publication on low-resolution sign language recognition in SIViP.

I enjoy combining practical engineering with research-minded thinking: understanding the problem deeply, choosing simple tools when they are enough, and using advanced systems when the product truly needs them.

What I'm looking for: I am looking for software development, machine learning, data, and cloud-focused roles where I can build scalable products and keep learning. I am especially motivated by teams that value clear communication, and high-quality execution.
02 — Experience

Building the Future

Professional roles focused on scalable systems and production software.

Software Engineer

Mar 2025 — Aug 2025
WILY PUMPKIN
Milan, Italy
Part-time · Early-stage startup
  • Developed scalable backend architecture and RESTful APIs using Django and gRPC to optimize high-traffic systems.
  • Managed cloud-based application on AWS using Docker containers, achieving 99.9% uptime and streamlined scaling.
  • Administered PostgreSQL databases and designed relational schemas, improving query performance and data storage.
  • Implemented CI/CD pipelines with Jenkins to automate deployment workflows and accelerate software releases.

Tech Stack

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

Software Engineer

Sep 2022 — Sep 2024 Ankara, Turkey
Full-time · Defense & IT — EyeMiner video analytics platform
  • 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.

Tech Stack

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

Machine Learning Engineer

Jun 2021 — Aug 2022 Ankara, Turkey
Full-time · AFIS fingerprint recognition system
  • 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.

Tech Stack

Python PyTorch OpenCV Anaconda Jupyter Notebook Jira Agile/Scrum

Software Engineer

Jun 2020 — Oct 2020 Istanbul, Turkey
Internship · National telecom infrastructure company
  • 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 streamline software delivery and release processes.

Tech Stack

Java MySQL AWS Docker Jenkins Git Linux Jira Agile/Scrum CI/CD
03 — Projects

Featured Work

Portfolio projects showcasing my work across backend, cloud and AI engineering.

Cognify

AI-powered Personal Knowledge Management System
GitHub ↗

Cognify is a full-stack personal knowledge management system — a second brain that actually reads your documents. Instead of manually tagging and filing notes, you drop in scanned PDFs, images, or plain-text files and an asynchronous Celery worker pipeline handles the rest: OpenCV preprocessing and Tesseract OCR extract text, DistilBART generates concise summaries, a zero-shot MNLI classifier auto-tags topics, and Sentence-BERT stores semantic embeddings — making every document instantly searchable and analyzable.

On the frontend, a Next.js 16 dashboard provides a knowledge vault overview, Recharts-powered analytics, browser-native PDF tools (split, merge, reorder), document versioning with parent-child lineage, per-document sensitive mode, soft-delete recovery, and a global RAG chat where RoBERTa generates cited answers from the most relevant chunks in your vault. The Django REST backend enforces JWT authentication, user-scoped data isolation on every query, audit logging for all actions, and Prometheus metrics for worker latency.

Key Capabilities

  • Full ingestion pipeline: upload → OCR → summarize → classify → embed → COMPLETED
  • Cosine similarity search finds meaning across documents, not just keyword matches
  • Collections, annotations, bookmarks, and notes attached to any document or quote
  • 35 automated tests, Docker Compose orchestration, MIT licensed

Tech Stack

Python Django Next.js Celery Transformers Sentence-BERT Docker Prometheus
Cognify knowledge vault dashboard

CryptMe

Secure Encrypted Messaging
GitHub ↗

CryptMe is a secure client-server messaging application built entirely in Java. It demonstrates practical cryptography in a real-time chat setting: users connect to a central server with unique usernames, join a shared chat room, and exchange messages encrypted with their choice of symmetric algorithm — AES (128-bit) with a 16-byte initialization vector, or DES (56-bit) with an 8-byte IV.

When a user selects an encryption method, the application generates a fresh secret key and IV for that session and distributes them to participants. The server handles connection management and message routing over TCP sockets without reading plaintext, while the Swing GUI client performs all encryption and decryption locally. A persistent log file records every generated key and IV for auditability.

Core Features

  • Dual encryption modes selectable at runtime — AES or DES
  • Multi-user chat with unique username registration and disconnect handling
  • Socket-based server routing encrypted payloads between connected clients

Tech Stack

Java AES DES Socket Programming Swing GUI
CryptMe live demo — encrypted messaging between two users
04 — Education

Scholarly Journey

Academic milestones that built my software, data, and ML foundations.

Master of Science - Computer Science and Engineering

Sep 2024 — Present Milan, Italy
Master's program

I am currently doing my Master's with a strong focus on Cloud Computing and Big Data. Most of my effort goes into distributed systems, scalable infrastructure, and hands-on technical projects where I can turn theory into practical solutions for complex, real-world software problems in production settings and large-scale platforms.

Relevant Coursework:
Software Engineering
Advanced Databases
Mobile Applications
Big Data Solutions
Computer Infrastructures
Streaming Data Analysis

Bachelor of Science - Computer Science

Oct 2017 — May 2022 Ankara, Turkey
GPA 3.33/4 · Honor Student

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.

Relevant Coursework:
Algorithms and Data Structures
Software Engineering
Databases
Database Management Systems
Machine Learning
Computer Vision

Erasmus+ Exchange Program

Sep 2019 — Feb 2020 Czestochowa, Poland
International exchange semester

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.

Relevant Coursework:
Applications of Artificial Intelligence
Machine Learning
Computer Vision
05 — Publication

Papers I'm Proud Of

Research focused on practical computer vision in low-resolution settings.

Tinysign: Sign Language Recognition in Low-Resolution Settings

Jun 2024
Signal, Image and Video Processing (SIViP) · Springer Nature
London, United Kingdom

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

  • 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.
06 — Skills

My Toolbox

Technologies and skills I use to build and run real production systems.

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

Expertise Areas

These are the areas where I have spent the most hands-on time through professional work, research, and academic projects.

Machine Learning
Computer Vision
Backend Development
Cloud Computing
Big Data

How I apply them

I connect these areas by designing end-to-end backend systems that ingest, process, and expose data with reliability and performance in mind. In practice, this means building stable APIs, scalable data pipelines, and cloud-native services that continue to work under real production pressure.

My strongest contributions are in projects where infrastructure, data engineering, and applied machine learning must operate together as one system. I focus on making these components measurable, maintainable, and ready for long-term use in production environments.

Turkish Native
English C1 — Professional
Italian B1 — Intermediate
08 — Contact

Get in Touch

Whether you want to discuss an opportunity, collaborate on a project, ask about my research, or simply connect, I would be happy to hear from you.

Socials