Job Title

About the RoleWe need a Junior AI Engineer who can do two things well: ship full stack web apps quickly using AI tools, and help maintain and improve our AI-powered automations, agents, and workflows.You don't need to architect complex RAG pipelines from scratch or build multi-agent systems on day one. But you should understand how these systems work, be comfortable maintaining and improving them, and be eager to grow into building them independently.What You'll Do- Build websites, MVPs, and product features fast using AI-assisted development — you'll often go from idea to working demo in a day- Help maintain and improve existing AI automations and agent workflows — monitor them, fix issues, and make incremental improvements- Assist in building and maintaining our RAG-based AI support chat — updating knowledge bases, testing accuracy, and improving responses- Set up and maintain automations using tools like n8n, Make, or Zapier to connect our internal tools and workflows- Integrate with SaaS tools our team uses — CRM, email, Slack/WhatsApp, ticketing, spreadsheets, and databases- Work alongside our senior engineers, learning how production AI systems work while contributing real, shippable code every dayWhat We Need From You- Full stack web development skills — you can build a working web app from frontend to backend. React/Next.js, Node.js or Python, and a database (PostgreSQL or similar). Doesn't need to be 5 years of experience, but you should be able to ship a complete feature on your own- AI-powered development workflow — you use AI coding tools (Claude, Cursor, Copilot, or similar) daily and they genuinely make you faster. You can prototype an MVP in hours, not days- Basic understanding of LLMs and AI agents — you know what prompt engineering is, you understand how tool calling works, and you've at least experimented with building simple agents or automations using LLM APIs- Basic understanding of RAG — you know what embeddings, vector search, and chunking mean. You don't need to have built a production RAG system, but you should understand the concepts well enough to maintain and improve one- Python skills — you can write scripts, work with APIs and JSON, and build simple backend logic- Willingness to learn fast — you'll be working with production AI systems from day one. We'll teach you, but you need to pick things up quickly and not wait to be told what to doNice-to-Haves- Experience with automation platforms (n8n, Make, Zapier)- Familiarity with LangChain, LlamaIndex, or similar frameworks- Basic knowledge of vector databases (FAISS, Qdrant, Pinecone, pgvector)- Experience with open-source LLMs (running local models, understanding tradeoffs vs. commercial APIs)- Docker basics and any cloud deployment experience (AWS/GCP)- MCP (Model Context Protocol) awarenessYou'll Do Well Here If...- You learn by doing — you'd rather build a rough version than read documentation for a week- You're excited about AI and already spend personal time experimenting with new tools and models- You're not afraid to ask questions, but you also try to figure things out yourself first- You care about shipping working software, not just writing clever code- You communicate clearly — you say what's working, what's stuck, and what you need- You want to grow into a strong AI engineer and are looking for a team that will push you