AI Full Stack Developer / Architect (ON-SITE ONLY)

Woongjin•San Jose, CA

2d•Onsite

About The Position

WOONGJIN, Inc. is seeking a highly motivated AI Full Stack Developer to join their team. The role involves building, deploying, and maintaining end-to-end applications that leverage generative AI models and agentic architectures. This position bridges the gap between AI research and production-ready applications, working across the entire stack from frontend interfaces to backend logic and machine learning models. The developer will be responsible for building, testing, and scaling AI-driven products.

Requirements

Proven experience (5-8 years for Middle Level & 9+ years for Sr. Level) as a Full Stack Developer with specialized experience in AI model deployment.
Strong proficiency in Python and frameworks like FastAPI or Django.
Experience with modern JavaScript frameworks (React.js + Node.js, Next.js, Plotly Dash + FastAPI).
Familiarity with AI model integration (e.g., OpenAI API, LangChain, PyTorch).
Experience in Cloud platforms (AWS, GCP, Azure) and container technologies (Docker, Kubernetes).
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field.

Nice To Haves

Experience with generative AI and agentic architectures.
Understanding of data privacy and security in AI applications.
PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.

Responsibilities

Develop and maintain end-to-end AI applications, from user interfaces to backend logic, focusing on AI-powered features.
Implement machine learning models (using frameworks like PyTorch, TensorFlow, or scikit-learn) into web and mobile applications.
Build and maintain scalable backend services and RESTful APIs, often integrated with large language models (LLMs) and agentic frameworks.
Create interactive, responsive front-end components for user interfaces using modern frameworks like React, Vue, or Next.js.
Manage end-to-end life cycles for production, including deployment workflows using Kubernetes, Cloud Run, or containerization tools to ensure high-performance applications.
Manage both relational and NoSQL databases to support AI-powered functionality.
Work closely with data scientists, product managers, and designers to turn AI capabilities into user-focused products.