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DENYSENKO VIACHESLAV

ABOUT
Motivated Junior Java Developer with a solid foundation in OOP, Java
Core, and practical experience in building web applications using Spring
Boot. Proficient in designing relational databases (SQL/PostgreSQL) and
managing version control with Git. I am deeply interested in the
CONTACT architecture of maintainable backends and building reliable orchestration
systems for AI and Large Language Models. Quick to learn and highly
[открыть контакты](см. выше в блоке «контактная информация») adaptable, I am looking to join a production team where I can deliver
business value while continuously elevating my engineering skills.
[открыть контакты](см. выше в блоке «контактная информация»)

https://t.me/slavik_denysenko WORK EXPERIENCE

https://github.com/slavikDe WinWin Travel (Non-commercial) 08.02.2026 - PRESENT
Java developer
SKILLS Participated in the design and development of the backend
architecture for a non-commercial travel platform using Java 17 and
Java - core, multithreading, streams, Spring Boot.
Developed clean, modular RESTful APIs for managing trip bookings,
Spring(boot, core, data), gc, memory
search filters, and user authentication processes.
Python basic Collaborated with the team within an Agile environment (Kanban) to
DB - SQL/NoSQL, vector/object systematically refine project requirements and ensure timely delivery
of software features.
Project architecture - monolith,
microservices, event-driven, AI PET PROJECT (DIPLOMA WORK)
micromonolith
https://gitlab.com/Thebestolo4b/orchestrator

AI - llm, agents, orchestrator, context Engineered a modular backend orchestrator utilizing Java 17 and
Spring Boot to seamlessly integrate and manage Large Language
window(lost in the middle), server
Models (LLMs).
inference Implemented local server inference using llama.cpp alongside
models sourced from Hugging Face (Llama 3, Qwen 2.5), eliminating
AI tool - claudeCode, codex, openAPI
external API costs and ensuring data privacy.
definition, skills, agent, rules Developed an advanced Retrieval-Augmented Generation (RAG)
Data Structures - array, list, tree, map, pipeline optimized for hierarchical summarization, enabling deep
semantic analysis of multimodal documents via vector databases.
table, hash, complexity Designed a dynamic Tool Selector mechanism that allows the AI
Search alg - quick, buble, binary, DFS, agent to intelligently choose and execute specific system tools or
APIs based on user intent.
BFS, A*, D*, ant-colony optimization Containerized the entire infrastructure (LLM servers, vector stores,
Env - git, maven/gradle, linux arch- and object storage) using Docker Compose for reliable, reproducible
local development and deployment.
base manjaro, kanban, atlassian
EDUCATION
LANGUAGES Bachelor of Science in Software Engineering 2022 - 2026
Igor Sikorsky Kyiv Polytechnic Institute | Kyiv, Ukraine
English (Upper intermediate) Faculty: Faculty of Informatics and Computer Science (FIOT)
Ukrainian (Native) Specialization: 121 Software Engineering