Java Fullstack Developer C2C jobs with AI Location : Charlotte-NC

Title : Java Fullstack Developer with AI

Location : Charlotte-NC (Hybrid) Local or near by

Contract

Exp:: 10+ years

Key Responsibilities

Design, implement, and maintain Spring Boot and Spring-based backend services, microservices architectures, and API gateways.
Architect and develop RESTful APIs, addressing versioning, security, payload design, and potentially SOAP/XML-based services as required.
Integrate AI and LLM-driven features using Spring AI or custom AI integrations, encompassing prompt management, embeddings, retrieval-augmented generation (RAG), chaining, caching, and model orchestration.
Develop frontend/UI layers with frameworks such as React, Angular, Vue, and ensure integration with backend APIs.
Build, maintain, or extend desktop/client applications where applicable, ensuring interaction with backend services.
Work on client-side features including state management, offline logic, caching, and real-time updates.
Design and implement event-driven and messaging solutions using Kafka, Pub/Sub, RabbitMQ, JMS, managing event flows, error handling, retries, partitioning, and schema evolution.
Implement webhooks, subscription management, and external system callbacks.
Maintain observability through structured logging, metrics, tracing, health checks, alerts, dashboards, and diagnostics.
Collaborate with DevOps/SRE teams to define CI/CD workflows, deployment strategies, rollout pipelines, and operational readiness.
Conduct code reviews, guide architectural decisions, enforce design standards, and mentor engineers.
Participate in sprint planning, backlog refinement, design discussions, operational analysis, and process improvement.
Required Skills & Experience

Core Java / Backend: 7–10+ years of professional experience with Java and related ecosystems; proficiency in Spring Boot, Spring Framework, Spring Data, Spring Security, dependency injection, AOP, configuration, and modularization.
AI / LLM Integration: Experience integrating or developing AI workflows in Java applications; familiarity with Spring AI or similar frameworks/libraries for embedding models, prompt templates, vector stores, RAG, and LLM orchestration.
API / Integration: Substantial experience designing and consuming REST APIs, including versioned APIs, request/response patterns, and error handling. Experience with SOAP/WSDL/XML-based services is advantageous.
Messaging / Eventing: Practical experience with event-driven systems using Kafka, message queues, Pub/Sub, JMS, and understanding of retry mechanisms, partitioning, consumer groups, and schema management.
UI / Frontend: Experience with frontend frameworks (React, Angular, Vue, etc.), state management, API integration, component design, performance optimization, and responsive design.
Observability & Monitoring: Background in logging frameworks (e.g., Logback, Slf4j, Log4j2), metrics (Micrometer, Prometheus), tracing (OpenTelemetry, Zipkin, Jaeger), dashboards, alerting, and health endpoints.
Deployment & DevOps: Knowledge of CI/CD pipelines, containerization (Docker), orchestration (Kubernetes), infrastructure as code, and version control systems.
Design & Architecture: Competency in clean architecture, domain-driven design, modular systems, design patterns (repository, service, factory, strategy), dependency inversion, and separation of concerns.
Soft Skills: Ability to participate in design discussions, contribute to technical direction, break down problems, mentor team members, and communicate across cross-functional teams.
Preferred / Nice-to-Have Skills

Experience with Spring Cloud and microservices ecosystem components (config server, service discovery, circuit breakers, API gateways).
Familiarity with vector databases and embedding stores (such as Pinecone, Milvus, Redis vector).
Understanding of domain-driven design, CQRS/event sourcing, and reactive streams (e.g., Reactor, Spring WebFlux).
Awareness of security frameworks/identity protocols/OAuth/JWT/OpenID Connect in enterprise environments.
Exposure to serverless or cloud-native AI deployments (AWS Lambda with LLM, Azure AI, Google Vertex AI).
Experience with on-premises or hybrid deployments, legacy system integration, and modernization efforts.
Knowledge of multi-agent workflows, AI orchestration, prompt chaining, or agent frameworks.
Experience implementing feature toggles, A/B testing, or canary deployments.