An AI Application Full Stack Developer builds end-to-end software where artificial intelligence drives core functionality. They bridge the gap between frontend web interfaces, backend microservices, and AI/ML models (like LLMs or SLMs) to turn generative capabilities into scalable, production-ready business solutions. [1, 2]This template outlines key responsibilities, requirements, and the technical stack for the role. [1, 2]Key Responsibilities

• End-to-End Development: Design, build, and maintain full-stack web or mobile applications powered by generative AI and agentic systems. [1]
• AI & API Integration: Integrate third-party foundational models (e.g., OpenAI, Anthropic, Hugging Face) and build AI-driven workflows like prompt orchestration and RAG pipelines. [1]
• Data & Persistence: Build and manage databases (SQL and NoSQL), including specialized vector databases required for smart, context-aware retrieval. [1, 2]
• Backend Architecture: Write robust, secure, and scalable backend services to support multi-step AI tasks, asynchronous workflows, and microservices. [1, 2, 3, 4, 5]
• AI-First UI/UX: Create responsive, intuitive frontend interfaces specifically designed to handle AI outputs, chat interactions, and real-time data. [1, 2, 3, 4]
• DevOps & MLOps: Implement CI/CD pipelines, containerization (e.g., Docker/Kubernetes), and performance monitoring to deploy and manage intelligent systems in production. [1, 2]

Required Technical Skills

• Frontend: Strong proficiency in modern JavaScript/TypeScript frameworks such as React, Next.js, or Vue.
• Backend: Advanced skills in server-side languages, heavily leaning towards Python (due to its ML ecosystem), Node.js, Golang, or Java.
• AI & Machine Learning: Familiarity with AI/ML frameworks (e.g., TensorFlow, PyTorch) and working with LLMs, prompt engineering, and agentic architectures.
• Data Management: Experience with traditional databases (PostgreSQL, MySQL) and specialized vector databases (e.g., Pinecone, Qdrant).
• Infrastructure: Cloud deployment proficiency (AWS, Azure, or GCP) and understanding of containerization (Docker, Kubernetes). [1, 2, 3, 4, 5, 6, 7]

Qualifications

• Experience: Typically 3+ years in full-stack web development, with documented experience integrating AI APIs or machine learning models into live products. [1, 2, 3, 4, 5]
• Education: Bachelor’s degree in Computer Science, Software Engineering, or equivalent practical experience. [1]
• Collaboration: Ability to work closely with data scientists, UX designers, and product teams to translate business needs into digital experiences. [1]