Full stack Java Engineer with AI expertise
About The Position
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 system s (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.
Requirements
- 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 system s (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 Haves
- 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 conten t filtering for responsible AI deployment
- Familiarity with Google’s agent/workflow tooling (e.g., Google Actions SDK or other Google-AI tooling).
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
