Senior Quant / Algorithmic Trading Engineer (Python, Crypto & AI)

We are building a next-generation trading system that combines classic quantitative methods with modern AI (LLMs and agents). I am looking for an experienced Quant / Algorithmic Trading Engineer to help design and implement the first production-grade version of this system. This is not a “toy bot” or signal channel. The focus is on: solid engineering, robust risk management, and verifiable, backtested performance. What you’ll be building A Python-based trading engine that can: Connect to one or more exchanges/brokers via API (initially crypto; later futures/FX/stocks). Ingest and store historical and live market data (candles, order books, trades). Run rule-based and quant strategies (long/short, leverage where appropriate). Execute orders reliably with proper logging, error handling, and safety checks. A research / backtesting workflow, including: Backtesting framework (Backtrader, vectorbt, Freqtrade, custom, etc.). Walk-forward testing and out-of-sample validation. Basic performance analytics: win rate, Sharpe, max DD, exposure, etc. An initial strategy set, e.g.: 1–3 “production-candidate” strategies (mean reversion, breakout/trend, volatility plays, etc.). Clear configuration and risk parameters (position sizing, per-trade loss caps, daily loss limits). Support for both paper trading and small-size live trading. An AI/LLM integration layer (Phase 2 of the contract): Use LLMs/agents for: monitoring and summarizing system health, generating reports on strategy performance, supporting idea generation and parameter search (human-in-the-loop). No “GPT decides trades”; AI is an assistive layer on top of real quant logic.

Responsibilities

Work with me to refine a realistic architecture and roadmap for the system. Implement clean, well-structured Python code for: data ingestion and storage, strategy execution and portfolio/risk management, exchange/broker API connectors (REST/WebSocket). Set up backtesting + paper trading environment and help define validation criteria. Prototype and implement 1–3 strategies from idea → backtest → paper → small live. Integrate LLMs/AI tools where they truly add value (e.g., using OpenAI API, LangChain, or similar) — not hype for its own sake. Document the system so it can be extended by additional team members later.

Requirements

Please only apply if you meet most of the following: Strong Python (data + backend): Pandas / NumPy, async IO, REST/WebSocket APIs, testing. Hands-on experience with algorithmic trading, ideally: Crypto and/or FX / futures (Binance, Bybit, OKX, BitMEX, Interactive Brokers, etc.). Practical experience with backtesting and live deployment. Familiarity with at least one trading/backtesting framework: Backtrader, vectorbt, Freqtrade, Zipline, QSTrader, custom, etc. Solid understanding of risk management: position sizing, leverage, drawdown control, kill-switches, etc. Comfortable designing and working with a data store: e.g., Postgres, DuckDB, or similar for storing historical data and results. Experience integrating LLMs or ML models into applications (nice to have but not strictly required if you’re strong on quant/infra and willing to learn). Soft stuff: Clear communicator in English. Comfortable collaborating over chat/voice a few times a week. Able to work independently, propose solutions, and push things forward without micro-management. Nice-to-have Prior work on a crypto trading bot or prop desk tooling. Experience with LangChain / crewAI / other agent frameworks. Experience deploying systems on cloud/VPS (Docker, Linux). Familiarity with event-driven architectures for trading systems. This is a hands-on engineering role. I’m not looking for a slide deck; I’m looking for working code, tested strategies, and a system we can build on. How to apply To help me filter out generic copy-paste proposals, please include the following in your application: A short description (2–3 sentences) of a trading system or bot you’ve worked on: What market(s)? What strategy type(s)? What was your exact role? What stack you would choose for: backtesting, live execution, data storage, and LLM integration — and why. One concrete example of a risk control you would implement from day one. Proposals without these answers will likely be ignored. If this sounds like something you’d enjoy building – and you have real experience shipping trading code, not just reading about it – I’d be happy to discuss further. Apply tot his job Apply To this Job