AI + Full-Stack Engineer

## About the Role We are looking for a highly capable AI + Full-Stack Engineering who can independently build complete AI-driven applications — from research and model integration to full-stack development, deployment, and optimization. You should be able to take a problem statement, choose the right AI approach, develop the frontend + backend, integrate APIs, test thoroughly, and deploy a polished product end-to-end without hand-holding. Note ( Important ) : Even though you do not have all the skills it is completely fine, but should be extermely curious and fast to adapt. ## Responsibilities (Combined AI + Full-Stack) - End-to-End Feature Development - Take complete ownership of building features — research, design, develop, test, and deploy AI-powered functionality. - AI Model Research & Integration - Evaluate and integrate relevant LLMs, vision models, or multimodal models (open-source or API-based) into production-ready applications. - Data Pipeline Preparation - Identify, collect, clean, structure, and preprocess datasets for training, fine-tuning, or retrieval-based workflows. - Model Training & Optimization - Fine-tune models, benchmark different configurations, optimize latency, accuracy, and cost, and ensure reliability in real-world scenarios. - Agentic AI Systems - Build autonomous agent workflows capable of reasoning, task execution, and tool usage using LangChain, CrewAI, AutoGen, or similar frameworks. - AI Infrastructure & Deployment - Deploy models on cloud platforms (AWS/GCP/Azure) or local inference servers, ensuring scalability and monitoring. - Build Robust Backend Systems - Design and develop backend services using Node.js/Express or Python frameworks, ensuring clean architecture and modularity. - Frontend Development & UI Integration - Build responsive user interfaces (React/Next.js or similar) and integrate them with AI-powered backend functionalities. - API & Third-Party Integrations - Integrate external APIs (AI APIs, authentication, payment, cloud services, etc.) to enhance product capabilities. - RAG, Vector Databases & Embeddings - Implement retrieval-augmented generation pipelines using vector databases like Pinecone, Chroma, or FAISS. - Testing & Debugging - Conduct unit, integration, and end-to-end testing, ensuring the system is stable and secure. - Performance Optimization - Optimize backend, frontend, and model inference layers for speed, scalability, and cost efficiency. - Deployment & DevOps - Deploy full-stack applications on platforms like Vercel, Netlify, Render, Railway, or AWS; set up CI/CD where needed. - Documentation & Collaboration - Maintain clear documentation for datasets, model configs, APIs, and architecture; collaborate with product and engineering teams. - Continuous Learning & Innovation - Stay updated on AI tools, frameworks, and full-stack tech; bring fresh experiments and ideas into the product. ## Preferred Skills - React / Next.js - Node.js / Express OR Python (FastAPI / Django) - Python for AI/ML - Experience with LLMs, Vision Models, or Multimodal Models - Vector databases (Pinecone, Chroma, FAISS, etc.) - LangChain / CrewAI / similar frameworks - AWS / GCP / Azure - Docker (optional but valuable)