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.

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