Key Responsibilities

Backend Development & Architecture (Java/Spring)

• Design, implement, and maintain high-volume, low-latency applications and RESTful APIs using Java 11+ and the Spring Boot ecosystem (Spring Data, Spring Security, Spring Cloud/Microservices).
• Collaborate with cross-functional teams (Product, Frontend) to define feature specifications and deliver technical solutions.
• Write comprehensive unit and integration tests (using JUnit, Mockito) to ensure code quality and maintainability.
• Optimize application performance, scalability, and security of the backend services.
• Manage relational and NoSQL databases (e.g., PostgreSQL, MongoDB), including schema design and query optimization via JPA/Hibernate.

AI Integration & Implementation [ Good to have or enthusiast to learn and adapt]

• Translate AI/ML requirements from stakeholders into scalable backend features (e.g., personalized recommendations, intelligent search, data classification).
• Integrate existing AI models and external LLMs (Large Language Models) into the Spring application using relevant tools like Spring AI or direct API calls.
• Work with Data Science/ML teams to ensure seamless deployment and monitoring of models via Java services.
• Develop Retrieval-Augmented Generation (RAG) patterns using vector databases and embedding techniques to ground AI results in proprietary data.

Required Skills & Experience (Mid-Level)

• 2+ years of professional experience in developing backend applications using Java.
• Strong proficiency with the Spring Framework, particularly Spring Boot for microservices architecture.
• Solid experience developing and consuming RESTful APIs.
• Experience with modern software development practices (Git, CI/CD, Agile/Scrum).
• Foundational understanding of AI/ML concepts and practical experience in integrating pre-trained models or utilizing GenAI APIs in a production environment.

Desired Qualifications (Bonus)

• Familiarity with containerization technologies (Docker, Kubernetes).
• Experience with event-driven architecture and message brokers (Kafka, RabbitMQ).
• Practical experience with Java-specific AI integration libraries like Spring AI or LangChain4j.
• Knowledge of Python/Jupyter for basic model testing or data analysis.