Who you are

• Expert Java engineering: Deep understanding of Java internals — GC tuning, Collections Framework, advanced concurrency (java.util.concurrent, multithreading), NIO/NIO2, performance profiling, and heap-dump analysis. Mastery of Spring Framework (IoC/DI, bean lifecycle, Spring Boot). SOLID principles, Clean Code practices, and GoF design patterns
• Software architecture and design patterns: Expertise in monolith and microservices architectural styles — including migration patterns and domain-driven decomposition. Inter-process communication design (REST, gRPC, messaging), transaction management in distributed systems (Sagas, 2PC), CQRS, Event Sourcing, and external API design focused on scalability, security, and documentation
• Cloud-native platforms and infrastructure: Experience designing high-availability and high-load systems on GCP (preferred), AWS, and Azure. Cloud security best practices: IAM, VPC, data encryption, JWT/JWS/JWE. Infrastructure as Code (Terraform or equivalent) and Twelve-Factor App methodology
• Observability, reliability and deployment: Implementing full observability stacks: structured logging, distributed tracing, metrics, and alerting. SLI/SLO/SLA frameworks. Deployment strategies: Rolling Updates, Blue/Green Deployments, Canary Releases
• AI-assisted engineering: Practitioner-level command of agentic AI tools applied to software engineering — encompassing prompt engineering techniques, AI context management and its limitations, sub-agents, skills and plugins, multi-agent orchestration, and team-of-agents architectures. Experience with Claude Code (Anthropic), Codex (OpenAI), or equivalent is mandatory

What the job involves

• The Software Engineer (Java) operates at a senior engineer level, driving technical direction across core product domains
• The role combines deep hands-on Java engineering with architectural ownership — from high-level system design and technology selection to leading delivery of critical, cross-cutting platform capabilities
• At this level, engineering excellence extends beyond implementation: the engineer determines the right approach to complex problems, communicates technical decisions across functions, participates in hiring, and is accountable for the long-term health of the systems they own
• This level demands sound judgment on technology and business trade-offs and the ability to act as a technical lead
• A defining expectation is mastery of AI-assisted engineering — leveraging agentic AI tools as force multipliers while retaining full ownership of architecture, quality, and technical outcomes
• Architecture and technology leadership: Lead high-level design for complex, cross-service features. Evaluate and select appropriate technologies, frameworks, and architectural patterns before delegating implementation. Produce and maintain architecture documentation: design docs, ADRs, tech specs, and wiki pages
• Advanced Java feature implementation: Own and implement critical product components — including prototyping, architecture validation, and production-grade code. Ensure correctness, performance, and long-term maintainability with comprehensive test coverage (unit, integration, contract, component)
• Technical roadmap contribution: Drive the engineering agenda for assigned product areas. Proactively identify gaps in requirements, architectural limitations, and technical risks. Contribute to product roadmap planning and delivery estimation. Participate in hiring processes
• Cross-functional technical communication: Drive technical communication across engineering, product, DevOps, and ML teams. Communicate technical decisions clearly to non-technical stakeholders. Produce design documents and participate in tech talks and knowledge-sharing sessions
• AI-augmented engineering: Direct agentic AI tools (Claude Code, Codex, or equivalent) across the full engineering workflow — code generation, testing, refactoring, debugging, and documentation. Demonstrated ability to apply advanced prompt engineering, manage AI context limitations, compose multi-agent orchestration workflows, and critically evaluate AI-generated outputs for correctness, security, and quality. Ability to establish guardrails and improve agent configurations to raise the quality bar. AI proficiency amplifies — it does not replace — deep engineering judgment and technical accountability

The application process

• Our selection process is designed to rigorously assess a candidate’s depth of technical knowledge, problem-solving ability, and alignment with Behavox’s mission and core values
• As part of the process, candidates will first participate in a series of interviews focused on evaluating their technical expertise and engineering judgment
• Candidates who successfully progress through these interviews will then be invited to complete a live technical exercise
• The purpose of this live technical assessment is to validate the candidate’s stated technical competencies and assess their ability to solve complex problems with speed, accuracy, and sound engineering judgment
• Note that whenever possible, we aim to conduct interviews in person at our offices
• We recognise and respect the time candidates invest in this process. In return, Behavox commits significant time and resources to ensure that those who join us have the capability, judgment, and alignment required to operate at the speed and level of complexity our work demands
• We value efficiency and clarity on both sides; if at any point we determine that a candidate is not a fit, we reserve the right to immediately conclude the interview or the technical assessment

Benefits

• Equity options
• Health insurance for the whole family, free of charge
• World-class relocation program with visa and citizenship sponsorship
• Great office culture
• OKR-based management