Have a working Python trading bot (IBKR, ib_insync))

I have a working Python-based algorithmic trading bot that needs a hands-on quant developer to deploy, monitor, tune parameters, and make incremental improvements. The strategy exists and is validated. This is NOT a strategy research role. What you'll do: Deploy and operate Python trading bots against Interactive Brokers (Gateway API, ib_insync) Monitor live and paper trading sessions, diagnose order rejections, handle partial fills, stale state, and position reconciliation Tune strategy parameters (entry thresholds, position sizing, risk gates) based on performance data Write and run backtests against historical tick/bar data Maintain cron jobs, watchdog scripts, and systemd services that keep the bot alive Coordinate with me on risk limits, symbol selection, and deployment decisions Infrastructure you'll work on: Dedicated bare-metal server: dual Xeon E5-2690 v4 (28 cores / 56 threads), 128GB DDR4 ECC, NVIDIA Tesla P40 (24GB VRAM) 2x 2TB NVMe hot storage, 4x 4TB HDD cold storage Ubuntu 22.04, Python 3.10, IB Gateway 10.37 with IBC for headless auto-login Ollama LLM stack for local AI-assisted analysis (not client-facing) SSH access provided, you will work directly on the box Required: Live IBKR deployment experience (not just backtesting) Strong Python (asyncio, pandas, numpy) Linux server administration (systemd, cron, SSH, screen/tmux) Understanding of execution risks: slippage, queue priority, order types (MOC, LOC, limit, market), partial fills Can read an existing codebase and explain the logic back to me before touching anything Nice to have: Experience with ITCH/market data feeds Familiarity with ib_insync library specifically Risk management implementation (drawdown locks, per-symbol halts, EOD flatten logic) Git workflow This is not a good fit if: Your experience is only backtesting or Jupyter notebooks You want to build a strategy from scratch (the strategy exists)