Java Developer with AI Integration

Quick Facts

• Role: Java Developer with AI Integration

• Experience: 7+ years

Description

Design, build, and scale high-performance Java microservices while embedding Generative AI capabilities into enterprise platform features. Implement GenAI patterns (RAG and agentic workflows) using Spring AI, LangChain4j, or direct LLM APIs, and architect robust REST and message-driven architectures. Deploy, monitor, and improve services on cloud using Docker, Kubernetes, CI/CD, and observability tooling.

Role Description & Responsibilities

• Build scalable, secure, resilient microservices with Java (JDK 17+) and Spring Boot

• Integrate LLM features such as automated workflows, conversational assistants, and semantic search

• Create and optimize RAG pipelines, including vector embeddings, chunking strategies, and vector database integrations (PostgreSQL/pgvector, Pinecone, Milvus, Redis)

• Develop agentic workflows/tools using function calling, MCP, state management, and robust fallback/error-handling

• Architect high-throughput RESTful APIs and asynchronous message-driven architectures with Kafka or RabbitMQ

• Perform thorough code reviews, including evaluation of AI-generated code for security vulnerabilities, hallucinations, and logic flaws

• Ensure low latency and follow secure coding practices

• Deploy, monitor, and scale on AWS/Azure/GCP using Docker and Kubernetes

• Use CI/CD pipelines (GitHub Actions, Jenkins) and Terraform for infrastructure-as-code

• Apply observability tools (Grafana, Splunk, Datadog)

Requirements

• 5+ years backend software development (hands-on)

• Deep expertise in Core Java (concurrency, collections, JVM tuning, memory management)

• Strong mastery of Spring Framework, Spring Boot, and Spring Cloud

• Practical experience integrating AI frameworks (Spring AI, LangChain4j) into enterprise backend applications

• Experience with asynchronous messaging systems (e.g., Apache Kafka)

• Proficiency with relational databases (PostgreSQL, Oracle, MySQL) and optimized SQL and/or Hibernate/JPA

• Experience deploying containerized microservices on AWS, Azure, or GCP using Docker and Kubernetes

• Familiarity with Terraform and observability tools (Grafana, Splunk, Datadog)

• Experience building multi-agent systems or complex tool-invocation loops

• Frontend exposure (Angular or React)

• Familiarity with AI pair programming/code-assist tools (GitHub Copilot, Claude Code)

Benefits

Not specified in the provided posting.