I’m scaling my trading institution and need hands-on talent across the entire electronic trading stack. The work covers multiple roles all related to trading via algorithm / AI: quantitative strategy coding, market-data engineering, low-latency execution, risk controls, large-scale back-test simulation, research platform tooling, MLOps pipelines, micro-structure optimisation, advanced data analytics and FPGA acceleration. You can focus on your strongest niche or take on a broader remit—as long as you already have production experience inside a finance-sector environment and can demonstrate battle-tested results. Code quality, reproducibility and operational safety are non-negotiable, so I’ll look for evidence of rigorous version control, unit testing and live-to-backtest parity. Typical deliverables we’ll scope together include: • A clean, well-documented module (Python, C++, or Verilog/VHDL where relevant) that slots into the existing code base. • A back-test that matches live fills within agreed slippage bands. • Automated CI/CD or MLOps workflow that promotes research models to production with roll-back capability. If you’ve shipped latency-sensitive code, built risk engines, streamed tick data at scale or deployed machine-learning models in live trading, let’s talk specifics and carve out a milestone that plays to your edge. You must have prior experience in this field and be able to deliver proven results ideal candidates can remain freelance or join my team and earn performance related bonuses