About the role
- Design, build and maintain REST APIs and microservices using Java and Spring Boot.
- Integrate LLMs (such as GPT and Claude) into backend workflows using prompts, function/tool calling and structured outputs.
- Build agentic workflows and agent harnesses: tool and function calling, planning loops, memory and context management, guardrails and human-in-the-loop checkpoints, with RAG as just one of the agent's tools.
- Make agents reliable: write evals, add tracing and observability, track cost and latency, and analyse failures so agent behaviour improves release by release.
- Use AI coding assistants daily and review their output critically for correctness, security and maintainability.
- Write clean, tested code: unit and integration tests with JUnit and Mockito.
- Work with databases (SQL and MongoDB) and message queues for reliable, scalable processing.
- Debug production issues, read logs, and fix bugs with guidance from senior developers.
- Take part in code reviews, design discussions and sprint ceremonies, and document what you build.
- Solid Java fundamentals: OOP, collections, exceptions, multithreading basics, and Java 8+ features (streams, lambdas, Optional).
- Working knowledge of Spring Boot: REST controllers, dependency injection, Spring Data JPA, configuration and basic security.
- Understanding of REST API design, JSON, HTTP and basic microservices concepts.
- Good grasp of SQL and relational database design; basic awareness of NoSQL (MongoDB).
- Hands-on exposure to agentic AI: you have built something beyond a basic chatbot or RAG demo, such as an agent that calls tools, uses MCP or orchestrates multi-step tasks with the OpenAI or Anthropic APIs.
- Familiarity with Git and basic command-line use.
- Strong data structures and problem-solving skills.
- Clear communication, a habit of asking good questions, and a willingness to learn quickly.
- Startup mindset: you take ownership, are comfortable with ambiguity, move fast without cutting corners on quality, and are happy to wear many hats (coding, testing, debugging, even talking to clients).
- A completed internship in backend or AI/ML development (a strong plus, not mandatory).
- Personal or academic projects on GitHub, ideally a Spring Boot app or an LLM-powered tool.
- Experience with agent frameworks and protocols (Model Context Protocol, Spring AI, LangChain4j, LangGraph, Claude Agent SDK), multi-agent orchestration, and RAG with vector databases such as pgvector, Pinecone or FAISS.
- Knowledge of Docker, CI/CD (for example Jenkins or GitHub Actions) and cloud basics.
- Familiarity with Angular or another frontend framework, and with Postman or Swagger/OpenAPI.
- Open-source contributions, hackathons or coding competition results.
- Real exposure to production systems, client projects and modern AI tooling instead of toy tasks.
- A clear growth path from junior developer to feature owner as your skills develop.