Design and implement: Build scalable, high-performance, and secure features for our Time & Attendance engine, including time-tracking, scheduling rules, overtime logic, and labor compliance calculations.
Leverage AI tooling daily: Use AI coding assistants and LLM-powered tools throughout your workflow — code generation, test scaffolding, code review acceleration, documentation, and debugging — and share learnings with your team.
Own quality: Write comprehensive unit tests, integration tests, and collaborate on automated test suites. AI-generated code still needs rigorous human review and validation.
Drive technical decisions: Participate actively in architecture discussions, propose solutions, and document trade-offs with clarity.
Mentor and review: Give substantive code reviews, pair with junior engineers, and model best practices including responsible AI-assisted development.
Collaborate cross-functionally: Work closely with Product Management, UX, and QA to translate business requirements into robust technical specifications and BDD scenarios.
Troubleshoot production issues: Diagnose and resolve complex bugs in distributed, high-volume environments with urgency and care.
Continuously improve: Identify and drive performance, scalability, and reliability improvements across the platform.
Uphold security standards: Participate in security reviews and ensure implementations comply with industry standards and internal policies.

Experience: 5-10+ years of professional software development with deep expertise in Java (or C++) in enterprise or SaaS environments.
AI tooling proficiency: Demonstrated, habitual use of AI coding assistants (e.g., GitHub Copilot, Cursor, Claude Code, ChatGPT, Tabnine) in daily development
you should be able to describe specific workflows where AI tooling materially changed how you work.
Backend depth: Strong command of Spring Boot, REST API design, Hibernate/JPA, and relational databases (MySQL or equivalent), including query optimization and indexing strategies.
Distributed systems: Solid understanding of microservices architecture, asynchronous patterns, and high-volume transaction handling.
Cloud & containerization: Hands-on experience with AWS and containerized deployments (Docker, Kubernetes).
Software craftsmanship: Deep familiarity with coding standards, code review practices, CI/CD pipelines, and DevOps principles.
Testing discipline: Proven ability to write and maintain unit tests, integration tests, and automated test suites
including for AI-generated code.
Problem-solving: Demonstrated ability to troubleshoot and debug complex, multi-layered systems independently.
Education: Bachelor's or Master's degree in Computer Science, Computer Engineering, or equivalent practical experience.

Senior Software Engineer

Key skills