Full stack Java Engineer with AI expertise

For one of our ongoing multiyear project out of Charlotte NC we are looking for a Full stack Java engineer with AI Expertise

Must-have skills & experience

• 3-6 years of hands-on experience building full stack applications using Java and the Spring Boot framework (or equivalent) in a production environment.

• Solid backend development skills: Java 8 Spring Boot RESTful APIs data access (JPA/Hibernate) relational databases (e.g. PostgreSQL MySQL) and familiarity with NoSQL as a plus.
• Frontend experience: delivered client side UI using frameworks like React (strongly preferred) or Angular/Vue with good working knowledge of HTML5 CSS JavaScript/TypeScript.
• Hands-on experience with AI workflows: developing agents working with LLMs integrating AI capabilities into applications (e.g. prompt engineering model orchestration)
• Experience taking an AI-centric systems into production: build deploy monitor troubleshoot live services handle performance scalability stability.
• Familiarity with enterprise-grade practices: version control (Git) CI/CD pipelines automated testing (unit integration) code reviews agile methodologies.
• Experience building event-driven or streaming systems (Kafka Reactor etc.).
• Experience with containerization and orchestration (Docker Kubernetes) or cloud deployments.
• Hands-on developing front-end/back-end interactions in the context of AI workflows (UI for model output integrations).
• Understanding of architecture in enterprise settings: microservices or modular architectures ability to work within a larger ecosystem of services dependencies security and operations concerns.
• Excellent problem-solving skills able to diagnose issues in production systems and propose solutions.
• Good communication skills: work across teams (DevOps QA product architecture) and clearly articulate technical trade-offs.

Nice-to-have / differentiators

• Implementing retrieval-augmented generation (RAG) systems with vector databases and semantic search

• Building multi-modal AI systems integrating text image audio or video processing
• Experience with AI safety techniques including constitutional AI red teaming and alignment evaluation
• Building AI agent frameworks with tool use planning and memory capabilities
• Implementing human-in-the-loop systems for continuous model improvement and feedback collection
• Knowledge of AI governance model versioning and experiment tracking in production environments
• Building robust prompt engineering frameworks with versioning and A/B
• testing capabilities
• Experience with LLM observability monitoring token usage latency and quality metrics in production
• Implementing guardrails and content filtering for responsible AI deployment
• Familiarity with Googles agent/workflow tooling (e.g. Google Actions SDK or other Google-AI tooling).