Senior Software Engineer
Engineer with a strong
product mindset
Java backend engineer building high-load systems and internal products end to end.
Over 10 years of experience in web backend development: fintech, e-commerce, logistics and manufacturing.
- About me
Senior Software Engineer with 10 years of experience in web backend development, 5 of them in high-load distributed systems. Java is the main ecosystem (Spring, Postgres, Redis, Kafka, Docker, Kubernetes), with Kotlin and Go used in production.
The work so far spans fintech, e-commerce, logistics and manufacturing, where reliability and performance are critical.
Product thinking sets the approach apart: start from the big picture, and when a simple task has potential, turn it into a valuable business product and take it end to end, from idea to production. That covers user research, stakeholder buy-in, UI/UX, architecture and backend.
Software is a living system, so decisions that let it evolve, scale and stay maintainable come first.
The current focus is AI: introducing AI agents into the payment integration process and building open-source tooling for agents that work with Java code.
My professional experience
01
Senior Java Software Engineer
(2023 – Present)
Built an internal product that automates payment integrations: analysts fill a structured spec and about 95% of the integration code is generated. 150+ integrations delivered; developer time per integration 30h → 6h, bug rate 2x lower; now the standard for all new integrations.
Introducing AI agents into the process: LLM-based spec review in production, AI analysis of provider documentation, open-source MCP server for Java code.
Migrated key tables (~10M rows) for cryptocurrency support with zero downtime; key domain expert in the core DDD monolith.
Mentored 4 engineers, regular code reviews. Team: 15 developers, 15 QA, 20+ analysts.
Dats.team
02
Senior Software Engineer
(2022 – 2023)
Built and ran high-load homepage services (up to 1,500 RPS at peak; Kotlin, Go, Java).
Built the pre-defined search tool: a microservice with an admin UI that fills promo pages automatically. Implemented A/B experiments in code.
Introduced code review and wrote the team guideline; mentored 2 engineers. Team of 12.
AliExpress Russia (Alibaba Group)
03
Senior Software Engineer
(2021 – 2022)
Designed and built, together with a colleague, a transportation request distribution module from scratch: 5–6 microservices, event-driven architecture, Kafka.
Processing time dropped from several days to 6 hours at most, lost requests down to zero. Team: 2 developers, 3 analysts.
Globaltruck IT
04
Middle Java Developer
(2020 – 2021)
Java backend for enterprise clients, including REST APIs and quality control systems for automotive logistics centers.
Digital League
05
PL/SQL Developer
(2019 – 2021)
Oracle E-Business Suite customization for a pharma plant: inventory, purchasing and warehouse management, direct communication with customers.
Earlier: Python Developer, 2016 – 2018.
Digital League
- Projects
Internal products and open-source tools
Integration Constructor · Dats.team
Problem: Every payment integration started with a free-form spec in Confluence and was coded by hand: slow and error-prone.
What I did: Built a structured web form for analysts with validation and a generator for classes, tests and configs; backend across 4 repositories. Ran user interviews, demoed it to the business, trained the users.
Result: 150+ integrations; developer time 30h → 6h, spec time 12h → 3h, bug rate 2x lower; 100% of new integrations go through it.
Stack: Java, Spring, PostgreSQL, template engine.
AI review of integration specs · Dats.team
Problem: One mistake in a signature or field mapping breaks an integration, and provider documentation is inconsistent (PDF, Swagger).
What I did: Built an LLM review of the spec against a checklist: parallel checks, a second model that cross-checks the verdicts, a report published to Confluence. Specified AI analysis of provider documentation.
Result: In production; about 20 specs reviewed so far, impact metrics are being collected.
Stack: Java, Spring AI.
LSP4J-MCP · Open source
Problem: AI agents navigate large Java repositories poorly.
What I did: Built an MCP server on top of the Eclipse JDT Language Server with about 20 tools (symbol and reference search, call hierarchy, diagnostics). One shared backend per repository serves all agents.
Link: github.com/saloidvl/LSP4J-MCP
Integration test automation · Dats.team
What I did: Built a Python tool for QA that creates the test context, configures the provider through the Admin API and exports variables to Postman. Trained the testers.
Result: Full testing cycle 18h → 6h.
Pre-defined search tool · AliExpress
What I did: Turned a one-line product idea into a microservice with an admin UI that fills promo pages automatically via the search service instead of manual product entry. Agreed the required changes with the search team.
- Writing
Notes on AI tooling for Java development
My AI toolbox, as a Java software engineer
A soft reminder: if you're using agents and not chatbots, you're already in a tiny minority on the bleeding edge.
Read on LinkedIn
My AI tools, part 2: ripgrep
Today's tool is ripgrep. Concept is simple: take grep, built by humans for humans, and adapt it for agents.
Read on LinkedIn
My AI tools, part 3: ripwire
Ripwire parses each file into an AST with tree-sitter, resolves it into a call graph, and indexes symbol names, doc comments and bodies.
Read on LinkedIn
How Netflix made AI coding agents more efficient with JetBrains IDE intelligence
JetBrains shared the results of using their tools on Java/Kotlin projects at Netflix. Typically 20-30% fewer tokens and less time.
Read on LinkedIn
Experience with real-world,
highly complex systems
Involvement in the development and evolution of critical systems across different sectors
Fintech & payments
Payment processing backends with external integrations, high availability and fault tolerance.
E-commerce & high-load
High-load backend services with A/B testing and modernization of legacy systems.
Logistics
Distributed event-driven systems, plus WMS and quality control systems for logistics centers.
Developer tooling & AI
Internal developer products and AI tooling: spec generators, AI review and an MCP server for agents that work with Java code.
Want to talk?
Manufacturing
Oracle E-Business Suite customization for a pharma plant: inventory, purchasing and warehouse management.
Tech stack
Technologies and approaches used in backend systems development
Also: Kotlin, Go, Redis, DDD, LLM agents, MCP
Java
Spring Boot
Kafka
PostgreSQL
Docker
