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**Job title: AI Full Stack Engineer - Contractor
Location: Charlotte, North Carolina, onsite
Role Description
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.
- Experience working in a large enterprise or complex organization (multiple teams, services, stakeholders).
- 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 modern AI workflows: developing agents, working with LLMs, integrating AI capabilities into applications (e.g., prompt engineering, model orchestration)
- Experience taking an AI-centric system into production: build, deploy, monitor, troubleshoot live services, handle performance, scalability, and 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 Google’s agent/workflow tooling (e.g., Google Actions SDK or other Google-AI tooling).
Tags & focus areas
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