Senior Full Stack Java Developer (AI Integration)

Introduction:

We are seeking an experienced Java Full Stack Developer with AI Experience to join our engineering team. In this role, you will design, build, and deploy high-performance enterprise applications while integrating modern Artificial Intelligence (AI) and Machine Learning (ML) capabilities. You will work across the entire software development lifecycle—from front-end user interfaces and back-end microservices to embedding Large Language Models (LLMs), vector databases, and AI workflows into production systems.

Responsibilities:

• Back-End Development: Design, build, and maintain scalable, secure back-end microservices and RESTful APIs using Java and Spring Boot.
• Front-End Development: Create responsive, intuitive user interfaces using modern JavaScript frameworks (React, Angular, or Vue.js).
• AI & LLM Integration: Integrate AI models, LLMs (e.g., OpenAI, Anthropic, Hugging Face), and AI frameworks (Spring AI, LangChain, LlamaIndex) into core applications.
• Data & Vector Search: Design schemas and optimize performance using relational/NoSQL databases alongside Vector Databases (e.g., Pinecone, Milvus, pgvector) for Retrieval-Augmented Generation (RAG) pipelines.
• System Architecture: Lead end-to-end architectural decisions, ensuring clean code, design patterns, microservices best practices, and secure AI deployment.
• CI/CD & DevOps: Automate build, testing, and deployment pipelines using Docker, Kubernetes, and cloud platforms (AWS, Azure, or Google Cloud Platform).
• Collaboration: Partner with product managers, UX designers, and data scientists to translate AI concepts into user-facing enterprise features.

Requirements:

Core Software Engineering:

• Java Mastery: 11+ years of hands-on Java development (Java 11/17/21), including Spring Framework, Spring Boot, and Hibernate/JPA.
• Front-End Expertise: 3+ years working with modern front-end frameworks (React.js, Angular, or Vue.js), TypeScript, HTML5, CSS3, and state management.
• Database & Storage: Strong experience with relational databases (PostgreSQL, MySQL) and NoSQL stores (MongoDB, Redis).
• Cloud & DevOps: Experience deploying scalable applications on AWS, Azure, or Google Cloud Platform using Docker and Kubernetes.

AI & Machine Learning Experience:

• AI Integration: Hands-on experience incorporating AI/ML services into production Java applications (e.g., using Spring AI, Python microservices bridging AI models, or REST API connectors).
• LLM & RAG Frameworks: Practical familiarity with Retrieval-Augmented Generation (RAG) architectures, prompt engineering, and vector databases (Pinecone, Qdrant, ChromaDB, or pgvector).
• AI APIs & SDKs: Familiarity with integrating foundation model APIs (OpenAI API, Claude, AWS Bedrock, or Azure OpenAI Service).