java engineering lead

Intellias helps the finance industry transform through engineering and develops solutions for leading FinTech companies. Its team builds a white-label application for managing Health Savings accounts.

• Lead a team of Java developers in designing, developing, and delivering high-quality software solutions that meet business needs
• Lead engineering delivery across the team’s roadmap, including AI-powered features and core platform work
• Own the engineering quality of AI-assisted features that handle sensitive data, including strict access controls that prevent AI tools from bypassing per-user and per-organization data boundaries
• Collaborate with stakeholders, product owners, and project managers to define project scope, goals, and deliverables
• Provide technical guidance, mentorship, and coaching to team members
• Conduct code reviews, enforce coding standards, and ensure code quality
• Participate in coding, testing, debugging, and deployment
• Work within the established microservices and Temporal-based workflow architecture using Java 25, Spring Boot, and AWS
• Keep up with Java development and AI integration trends and contribute to the technical roadmap

• At least 6 years of Java experience
• API design and documentation skills
• Ability to describe and document technical decisions
• Deep practical experience with Spring Boot
• Deep understanding of microservices architecture
• Knowledge of REST, gRPC, events, and messaging protocols
• Hands-on Docker containerization experience across different environments
• Understanding of CI/CD practices and quality gates
• Hands-on experience with unit, integration, and API testing for microservices
• Take full responsibility for the results of own work
• Upper-Intermediate English
• Available for international business travel
• Bachelor’s degree
• Будет плюсом: experience with durable workflow orchestration tools such as Temporal or Camunda; experience in regulated domains such as fintech or healthcare, including PCI DSS, HIPAA, or SOC 2; production experience building AI-powered features, including prompt engineering, LLM API integration, RAG pipelines, or embedding/vector-search retrieval; familiarity with agentic engineering patterns, tool-calling, function-calling, multi-step agent orchestration, and frameworks such as LangChain or LangGraph; understanding of AI engineering safeguards, including access-control scoping, auditability, traceability, and guardrails against unintended data exposure

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