Java Backend Engineer - AI Platforms

At Graphwise, we help enterprises transform fragmented data into connected, intelligent systems using Knowledge Graphs, semantic technologies, and modern AI architectures.

About the role

We're looking for a strong Java Backend Engineer who wants to build systems where Knowledge Graphs, LLMs, and custom ML models come together. You'll be designing and implementing backend services that orchestrate complex AI workflows for some of the largest and most prominent organizations in the world. You don't need to be an AI expert already – what matters most is solid engineering thinking, curiosity, and the ability to design clean, reliable systems that solve hard real-world problems.

Main Responsibilities:

• Design and build robust backend services that orchestrate Knowledge Graphs, LLMs, and custom ML models
• Implement scalable APIs and integration layers that connect AI components with enterprise systems
• Develop retrieval and context architectures, including RAG and GraphRAG patterns
• Contribute to the design of distributed, production-grade systems for demanding enterprise workloads
• Help shape how Knowledge Graphs interplay with modern AI in real-world deployments
• Collaborate with ML engineers, data engineers, and solution architects to deliver end-to-end functionality
• Collaborate with technical and business stakeholders to solve complex information challenges
• Explore and adopt modern AI, NLP, and backend engineering technologies
  
  

Must-haves:

• Strong software engineering fundamentals
• Strong analytical and problem-solving skills
• Professional experience with Java and Spring Boot or similar Java backend frameworks
• Experience designing and building backend systems, services, and APIs
• Solid understanding of good software design practices and distributed systems
• Experience with Git, CI/CD, and collaborative engineering practices
• Excellent communication skills in English
• Curiosity and willingness to learn new domains and technologies
  
  

Nice-to-haves:

• Cloud platforms, containerization
• Experience integrating LLMs or ML models into production backend systems
• Familiarity with modern AI concepts such as: LLMs, vector databases, embeddings, (Graph)RAG implementations