Artificial Intelligence Engineer

About the Role We 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 day

What 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 do

Nice-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) awareness

You'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