Artificial Intelligence Engineer

About the Role

We are looking for an ambitious and adaptable AI Software Engineer to help build our trading-intelligence platform. The product is a website plus a Windows desktop app. The public site is the landing page. Users download the app and run the product on their own machine. You will work with the engineer who owns the platform, across the full stack, and turn business needs into working software.This is a product-engineering role. You will use modern LLM APIs inside a live trading product. We are hiring early-career engineers: strong graduates, or people with about one to two years of work.What you will work on:Web: A Next.js and React site, covering the public landing page and the signed-in product for pre-trade analysis, trade review, and chat.Backend: Python services for market analysis, user accounts, and live broker data.Chat: A trading assistant that answers questions, uses market and account context, and streams a clear written reply.Desktop: An Electron app, shipped as a Windows executable, that users download from the site and use as their main way into the product.Markets: MetaTrader 5 for prices, charts, and account history across forex, stocks, commodities, crypto, and indices.Infrastructure: The live system on AWS, deployed and looked after from the Linux command line.

Key Responsibilities

Platform work: Improve the product wherever the work is: backend logic, the web app, the chatbot, or the desktop app.Backend: Build and maintain Python services for analysis, market data, authentication, and MT5.Chat: Help shape the assistant: how it understands a question, which tools it calls, and how it writes the answer.Desktop: Connect the desktop app to the backend, including authentication, updates, and safe use of model APIs.Trading integration: Integrate MT5 for prices, charts, account history, and position sizing. Treat live accounts with care, and change order flow only when that behaviour is explicitly in scope.Deployment: Help deploy and operate the stack on AWS EC2, including services, logs, and safe restarts.AI development tools: Use tools such as Cursor to move faster, and review what they produce before it ships.Collaboration: Work directly with the engineer who owns the platform and with the business. Explain trade-offs in plain language and ship small, testable changes.Experience:About one to two years in a software role, or a strong graduate with projects that stand in for that time. An internship counts.You have built something with a web frontend and a Python backend, at work, in an internship, or as a substantial project. You can read an existing codebase, follow how one service calls another, and make a small change without breaking the rest.You have called a hosted LLM from your own code. A course project, a side project, or a feature at work is enough. You know what a prompt, a tool call, and a streamed reply are, and you have seen a model answer badly and fixed the code around it.You are comfortable in a terminal. You do not need to have run production. You should be ready to deploy on a Linux server with someone who already knows the setup.A working interest in markets is enough. You should know what MT5 is, what a symbol and a timeframe are, and why account size matters for a trade. Brokerage experience is not required.Hard Skills & Qualifications:Architecture: You can look at a small system and explain where data lives, which part talks to the outside world, and what the website and the desktop app each do.Backend: Solid Python. Familiar with FastAPI, async code, Postgres, and Redis, from work or from projects you can talk through.Frontend: Hands-on React and Next.js with TypeScript. Familiar with Tailwind. You can build a screen and match an existing design.Desktop: You have used Electron, or a similar desktop stack, and you can learn how we package the Windows app.AI integration: You have used a hosted model API such as OpenAI, Gemini, or OpenRouter. You can change a chatbot’s behaviour in code, not only by rewriting a prompt.Markets: A basic grasp of forex, indices, commodities, and crypto, plus the idea of risk per trade. You understand why a trading assistant must not invent prices or place trades by accident.AWS and Linux: You can use the Linux command line. You have some exposure to cloud servers, or you can get comfortable on EC2 quickly with guidance.AI tools: You already use coding assistants in daily work, and you read the change before you keep it.Communication: You can explain a technical choice to someone who is not an engineer, and you can turn a business request into a small piece of working software.Nice-to-Haves:A small Electron app you can show.A chatbot that calls tools, even as a project.A deploy you did yourself, on any Linux host.Personal trading, a markets elective, or time around a broker platform.How the role works:You will pair with the engineer who already owns the platform. Expect to pick up a slice of work, such as a chatbot change, an analysis fix, a desktop update, or a screen in the app, and carry it through with review. You will learn the trading domain and the production setup on the job. Judgment still matters. Trading users act on what the product says..