Experience: 7+ Years
Visa: EAD, L2, E2, TN
About the Role
We are looking for an experienced Java Developer with AI Integration expertise to bridge the gap between enterprise-grade Java architectures and modern Artificial Intelligence. In this role, you will design, build, and scale high-performance microservices while embedding Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI workflows into core platform capabilities.
Key Responsibilities
Enterprise Java Engineering: Design, develop, test, and maintain scalable, secure, and resilient microservices using Java (JDK 17+) and Spring Boot.
AI & LLM Integration: Implement GenAI patterns using Spring AI, LangChain4j, or direct LLM APIs (OpenAI, Anthropic Claude, Gemini) to build intelligent features like automated workflows, conversational assistants, and semantic search.
RAG & Vector Data Pipelines: Build and optimize Retrieval-Augmented Generation (RAG) pipelines, managing vector embeddings, chunking strategies, and integrations with vector databases (e.g., PostgreSQL/pgvector, Pinecone, Milvus, Redis).
Agentic Workflows: Develop autonomous or semi-autonomous agent tools utilizing function calling, model context protocols (MCP), state management, and robust error-handling/fallback mechanisms.
System Design & API Contracts: Architect high-throughput RESTful APIs and asynchronous message-driven architectures using Kafka or RabbitMQ.
Code Quality & AI Governance: Conduct thorough code reviews (including evaluating AI-generated code for security vulnerabilities, hallucinations, and logic flaws), ensure low latency, and adhere to secure coding practices.
Cloud & DevOps: Deploy, monitor, and scale applications on cloud infrastructure (AWS / Azure / Google Cloud Platform) using containerization (Docker, Kubernetes) and CI/CD pipelines (GitHub Actions, Jenkins, Terraform).
Required Qualifications & Technical Skills
Core Java & Frameworks:
5+ years of hands-on backend software development experience.
Deep expertise in Core Java (Concurrency, Collections, JVM tuning, Memory Management).
Strong mastery of Spring Framework, Spring Boot, and Spring Cloud.
AI & Modern Architecture:
Practical experience integrating AI frameworks (Spring AI, LangChain4j) into enterprise backend applications.
Experience with asynchronous messaging systems (e.g., Apache Kafka).
Proficiency in relational databases (PostgreSQL, Oracle, MySQL) and writing optimized SQL queries/ORMs (Hibernate/JPA).
Experience deploying containerized microservices to AWS, Azure, or Google Cloud Platform using Docker and Kubernetes.
Familiarity with infrastructure-as-code (Terraform) and observability tools (Grafana, Splunk, Datadog).
Experience building multi-agent systems or complex tool-invocation loops.
Frontend exposure (Angular or React) for full-stack capabilities.
Familiarity with AI pair programming and code-assist tools (GitHub Copilot, Claude Code) to accelerate delivery workflows.
