Максим

Max Martseniuk CV
Khmelnytskyi [open contact info](look above in the "contact info" section) [open contact info](look above in the "contact info" section)

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Backend Developer with hands-on experience in building scalable REST APIs using Django. Proficient in integrating AI
features and developing robust backend architectures with PostgreSQL, Redis, Celery, and Docker. Brings additional technical
versatility through practical background in autonomous systems software (ROS2, Computer Vision, Reinforcement Learning).

EDUCATION
Bachelor's Degree in Software Engineering
Khmelnytskyi National University
Sep 2025 – Jun 2029 (Expected)
Associate Degree in Computer Engineering
Khmelnytskyi Polytechnic Professional College
Sep 2021 – Jun 2025

SKILLS
Backend: Python, Django, REST API, Celery.
Databases & Caching: PostgreSQL, Redis, MYSQL
AI & Computer Vision: YOLO
Robotics & Embedded: C++, ROS2, PX4
Infrastructure & Tools: Docker, Git, GitHub, Postman, AWS S3 (MinIO)
Frontend: React, HTML, CSS

LANGUAGES
Ukrainian: Native
English: B2 (Upper-Intermediate)

COURSES & CERTIFICATIONS
Python Basics | University of Michigan
Jan 2025 – Mar 2025
Django Web Framework | Meta
Mar 2025 – Apr 2025
APIs | Meta
Apr 2025

EXPERIENCE
Drone Software Developer (Intern) | Defense Tech Sector Oct 2025 – Apr 2026

During my six-month internship in the Defense Tech sector, I developed software for autonomous unmanned systems,
focusing on the integration of artificial intelligence into flight stacks. Using a combination of C++ and Python, I developed and
optimized modules for the Raptor system, which specializes in AI-driven compensation for external factors such as wind and
turbulence. My responsibilities included modifying the open-source rl-tools repository to implement reinforcement learning
algorithms specifically adapted for a hexacopter platform, ensuring stable communication via the uXRCE-DDS protocol, and
coordinating component interactions within the ROS2 environment. Additionally, I integrated YOLO computer vision models for
object recognition and conducted comprehensive testing and debugging of the architecture directly within the PX4 ecosystem,
achieving high-precision autonomous control in complex conditions.

PROJECTS
Notion-style Notes Workspace

Developed a full-stack Notion-style application for note creation and management. Built a comprehensive REST API using
Django, implementing change caching with Redis and deferred data saving to PostgreSQL via Celery. The frontend was
developed with React, HTML, and CSS, incorporating Tiptap as a Markdown editor. Implemented autosave logic on both the
client and server sides, utilizing debounce mechanisms and background tasks to optimize system load.

Zenith Fitness Hub (Full-Stack / AI Integration) — In Progress

Developing the backend architecture for an intelligent sports coaching platform. Designing a hybrid system utilizing Django for
core business logic and FastAPI to power high-performance AI endpoints. Currently integrating YOLOv11-based Computer
Vision for real-time biomechanics analysis from smartphone cameras, alongside Llama 3.2 with RAG for intelligent NLP-driven
analytics of the training process. Configuring an asynchronous video processing pipeline via Redis and Celery to ensure
seamless data handling, while fully containerizing the infrastructure using Docker.