Artificial Intelligence Engineer

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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).