AI Full Stack Developer & Architect

1 Yr Contract Onsite Mid- Level Pay Rate: ~$13,975/mo DOE We are looking for a highly motivated  AI Full Stack Developer  to join our team to build, deploy, and maintain end-to-end applications that leverage generative AI models and agentic architectures. As a Full-stack AI Developer, will bridge the gap between AI research and production-ready applications, working across the entire stack from frontend interfaces to backend logic and machine learning models. Will be responsible for building, testing, and scaling AI-driven products. AI Application Development:  Develop and maintain end-to-end AI applications, from user interfaces to backend logic, focusing on AI-powered features. ML Model Integration:  Implement machine learning models (using frameworks like  PyTorch ,  TensorFlow , or  scikit-learn ) into web and mobile applications. Backend & API Engineering:  Build and maintain scalable backend services and RESTful APIs, often integrated with large language models (LLMs) and agentic frameworks. Frontend Development:  Create interactive, responsive front-end components for user interfaces using modern frameworks like  React ,  Vue , or  Next.js . MLOps  & Deployment:  Manage end-to-end life cycles for production, including deployment workflows using  Kubernetes ,  Cloud Run , or containerization tools to ensure high-performance applications. Database Management:  Manage both relational and NoSQL databases to support AI-powered functionality. Collaboration:  Work closely with data scientists, product managers, and designers to turn AI capabilities into user-focused products. Required Qualifications & Skills Experience:   Proven experience(5~8 yrs. for Middle Level & 9+ yrs. for Sr. Level) as a Full Stack Developer with specialized experience in AI model deployment. Backend Skills:  Strong proficiency in Python and frameworks like FastAPI or Django. Frontend Skills:  Experience with modern JavaScript frameworks (React.js + Node.js,Next.js, Plotly Dash + FastAPI). AI/ML Knowledge:  Familiarity with AI model integration (e.g., OpenAI API, LangChain, PyTorch). Cloud/DevOps:  Experience in Cloud platforms (AWS, GCP, Azure) and container technologies (Docker, Kubernetes). Education:  Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field. Preferred Qualifications 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 All your information will be kept confidential according to EEO guidelines.