Java Developer

Why This Role Exists :

We're building backend systems that don't just serve data - they power intelligent, AI-driven experiences. We need a Java engineer who is solid on fundamentals, comfortable owning production services, and curious enough to work at the intersection of backend infrastructure and AI. You'll work on real systems, not prototypes. If you want depth, ownership, and exposure to where modern backend engineering is heading, this is that role.

What You'll Work On :

- Own and evolve data models, JPA-based persistence layers, and SQL-backed services that power core product features

- Integrate LLM-based capabilities into existing and new backend workflows

- Build scalable microservices with a strong focus on reliability, performance, and maintainability

- Design for concurrency, fault tolerance, and high availability across distributed systems

- Write clean, well-tested code unit tests, integration tests, and meaningful coverage are expected, not optional

- Work closely with product and senior engineers to translate requirements into well-architected solutions

- Participate in code reviews, system design discussions, and production incident resolution

What We're Looking For :

Core Backend Engineer :

- 3- 5 years of hands-on backend development experience

- Strong proficiency in Java with deep experience in Spring and Spring Boot

- Solid experience with JPA/Hibernate entity modeling, relationships, lazy loading, and query optimization

- Proven ability to design and build production-grade REST APIs and microservices

- Strong command of SQL query optimization, schema design, indexing, and working with relational databases (PostgreSQL or MySQL preferred)

- Solid understanding of object-oriented design, design patterns, and SOLID principles

- Experience with caching strategies, connection pooling, and performance tuning in backend services

- Familiarity with message queues or async communication patterns (Kafka, RabbitMQ, or similar) is a plus

- Good understanding of system design fundamentals scalability, availability, and data consistency tradeoffs

AI Integration :

- Comfortable working with AI workflows prompt engineering, chaining, or API-level integration

- General familiarity with how AI/ML systems work and how to build reliable wrappers around them

Engineering Discipline :

- Strong attention to code quality, testability, and observability

- Experience with logging, monitoring, and alerting in production environments

- Ability to debug and own issues end-to-end across the stack

Bonus Points :

- Familiarity with DevOps practices containerization (Docker/Kubernetes), pipelines, infrastructure basics

- Experience with event-driven architecture or microservices at scale

- Experience working in a fast-paced product or startup environment

What Success Looks Like :

- You take ownership of backend services and make architectural decisions with confidence

- You proactively think about reliability, scalability, and maintainability without being prompted

- You write code that other engineers enjoy reading and extending

- You become the go-to for backend quality and help raise the bar for the team

Why Hivel

exposure to AI systems early at a time when that intersection is where the most interesting engineering is happening.

If you want a predictable job, this isn't it. If you want accelerated growth and work that matters, this is it.