Build Java and Spring Boot REST APIs and microservices while integrating GPT and Claude into production agentic AI workflows. Develop tool-calling agents, planning loops, memory, guardrails, evaluations, tracing, and observability. Write tested code with JUnit and Mockito, work with SQL, MongoDB, and message queues, debug production issues, participate in reviews, and collaborate with senior engineers on client-facing products.
🚨 We’re hiring: Jr. Software Developer 🚨
(Java / Spring Boot & Agentic AI),
📍 Hyderabad (Work from Office)
🎓 0–2 years | Final-year students & fresh graduates welcome
About the roleYou will join our engineering team to build enterprise backend services in Java and Spring Boot, and to ship AI features on top of them: agentic AI systems: LLM agents that plan, call tools and complete multi-step tasks inside a production-grade agent harness, well beyond basic RAG and chatbots.
This is a hands-on role for someone who wants to learn fast. You will pair with senior engineers, work on real client-facing products from week one, and grow into owning features end to end. We care more about strong fundamentals and curiosity than years of experience.
We work like a startup: small teams, fast decisions and a lot of ownership. You will often start with an unclear problem, and we expect you to figure out the next step, build a working version quickly and improve it.
What you will do- 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.
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