An AI Full Stack Engineer (also known as Full-Stack AI Developer or AI Application Engineer) builds complete, end-to-end AI-powered applications. This role combines traditional full-stack development (frontend + backend) with AI/ML integration, enabling the creation of intelligent, production-ready products like AI chatbots, recommendation systems, intelligent dashboards, RAG applications, and agentic AI tools.
You will own features from UI/UX to data pipelines and model deployment — bridging software engineering, machine learning, and cloud infrastructure.
This is a high-demand, versatile role ideal for modern tech companies, startups, and enterprises adopting generative AI.
Key Responsibilities
- Design and develop responsive, user-friendly frontend interfaces using modern frameworks.
- Build robust backend services, APIs, and data layers to support AI functionalities.
- Integrate AI/ML models (LLMs, computer vision, recommendation engines) into production applications.
- Implement Retrieval-Augmented Generation (RAG), vector search, embeddings, and agentic workflows.
- Develop and optimize data pipelines that feed AI models (connecting to data lakes, warehouses, or real-time sources).
- Deploy, monitor, and scale AI applications using MLOps practices, containerization, and cloud services.
- Ensure performance, security, scalability, and ethical AI compliance (bias mitigation, data privacy).
- Collaborate with data scientists, ML engineers, designers, and product teams to deliver AI-driven features.
- Write clean, maintainable code and participate in code reviews, testing, and CI/CD pipelines.
Required Skills & Qualifications
- Frontend: React.js / Next.js, Vue, Angular, TypeScript, Tailwind CSS or similar.
- Backend: Python (FastAPI, Flask, Django), Node.js, or Go. Strong API development (REST, GraphQL).
AI/ML Integration
- Experience with LangChain, LlamaIndex, Hugging Face, OpenAI/Anthropic APIs.
- Basic model training/evaluation with PyTorch or TensorFlow.
Data & Infrastructure
- SQL/NoSQL databases, data lakes/lakehouses.
- Cloud platforms: AWS, Azure, GCP (especially AI services like Bedrock, SageMaker, Vertex AI).
- Other: Git, testing (unit + integration), observability (Prometheus, LangSmith).
- Experience: 3–7+ years in full-stack development, with at least 1–2 years in AI/ML integration.
- Education: Bachelor’s or Master’s in Computer Science, Software Engineering, or related field.
Nice-to-Have Skills
- Experience with LLMOps, AI agents, or multimodal AI.
- Domain knowledge in specific industries (healthcare, finance, e-commerce, etc.).
- Strong understanding of AI ethics, governance, and responsible AI practices.