Java Developer

emerchantpay is a leading global payment service provider and acquirer for online, mobile, in-store and over the phone payments. Our global payments solution is available through a simple integration, offering a diverse range of features, including global acquiring, global and local payment methods, advanced fraud management and performance optimisation. We empower businesses to design seamless and engaging payment experiences for their consumers.

We are looking for enthusiastic and experienced Java developers for one of our payment processing products that we are modernizing.

The role combines hands-on engineering with modern, AI-assisted development practices. You will build and scale highly available payment services, using AI coding agents and tooling as part of your daily workflow while keeping the quality, security and reliability that payments demand.

Responsibilities

•      Designing and implementing complex workflows, APIs, and UI backends for payment platforms;

•      Performing TDD using various tools like JUnit;

•      Taking part in scaling highly available payment services (99.99%+ uptime);

•      Using AI coding assistants and agents (e.g. Claude Code, Kiro, GitHub Copilot) for implementation, refactoring, test generation, code review and legacy code exploration, and owning the quality of everything they produce;

•      Contributing to the team's shared AI tooling, such as agent skills, project instructions (e.g. CLAUDE.md), prompt templates and MCP server configurations, so AI-assisted work is consistent and repeatable;

•      Applying secure and responsible AI practices in a PCI DSS environment, so that no cardholder data, secrets or other sensitive information reaches AI tools.

Requirements

•      9-10+ years using core Java and/or J2EE, Spring;

•      Knowledge in ORM solutions, e.g. Hibernate;

•      Database experience with open-source databases like MySQL and/or PostgreSQL;

•      Experience in JavaScript and decent front-end skills;

•      Comfortable with TDD/BDD practices, JUnit, etc.;

•      Hands-on, day-to-day experience with AI coding assistants or agentic tools (e.g. Claude Code, Kiro, Cursor, GitHub Copilot), including writing effective prompts and specs and critically reviewing AI-generated code;

•      Practical understanding of how AI agents connect to engineering tools through MCP (Model Context Protocol) servers, e.g. GitHub, Jira, Confluence, databases, and of the related risks such as prompt injection and over-permissioned access.