Founding Senior Python Platform Engineer — Algorithmic Trading

Senior Python Engineer — Algorithmic Trading Platform ZEUS Algorithmic Platforms Ltd Contract / Part-Time initially | Remote About ZEUS ZEUS is a proprietary algorithmic trading and quantitative research platform built to develop, validate, deploy, and operate systematic trading strategies. The platform spans the full strategy lifecycle: market data ingestion, signal evaluation, backtesting and deterministic replay, execution, position lifecycle management, operational controls, observability, and quantitative research. ZEUS is already operational and trading live strategies. We are now looking for an experienced Python engineer to support its continued development, hardening, and evolution. This is not a greenfield build. You will be joining an existing production system where reproducibility, deterministic behaviour, data integrity, and safe change management are critical. The Role We are looking for a strong Python engineer with experience building reliable data-intensive or event-driven systems. You will work directly with the founder to maintain and extend the ZEUS platform while preserving strict behavioural consistency between research, simulation, replay, and live trading environments. The role combines software engineering, data engineering, platform reliability, and quantitative trading infrastructure. You will not be expected to invent trading strategies or independently conduct quantitative research. Your primary responsibility will be ensuring that research and platform requirements are translated into reliable, testable, production-quality systems. What You’ll Work On * Maintain and extend a production Python algorithmic trading platform. * Develop and harden live trading runners and execution workflows. * Maintain integrations with broker and market-data APIs, including Interactive Brokers. * Improve deterministic replay, simulation, and live-vs-replay parity. * Build and maintain research data pipelines and normalized analytical datasets. * Implement strategy and research specifications accurately without introducing unintended behavioural changes. * Improve testing, reconciliation, telemetry, observability, and failure recovery. * Diagnose complex production issues involving order state, timing, concurrency, market data, and strategy lifecycle behaviour. * Strengthen disaster recovery and operational resilience. * Improve performance and reliability of large-scale historical simulations and research workloads. * Maintain clear technical documentation and architecture records. * Use AI-assisted development tools effectively while maintaining rigorous engineering review and validation. The Engineering Problems Some of the problems you may encounter include: * Guaranteeing deterministic behaviour across historical replay and live execution. * Preventing race conditions and duplicate evaluation in event-driven trading systems. * Maintaining immutable decision and evaluation histories. * Reconciling broker state with internal platform state. * Processing hundreds of thousands or millions of strategy evaluations reproducibly. * Designing resumable and provenance-aware quantitative research pipelines. * Safely evolving a live system without invalidating historical research or changing strategy semantics unintentionally. These are correctness problems as much as coding problems. Core Requirements You should have strong experience with: * Python in production environments. * Designing and maintaining non-trivial software systems rather than standalone scripts. * SQL and relational data modelling. * Data pipelines and large analytical datasets. * APIs and external system integrations. * Git and disciplined version-control workflows. * Automated testing and regression testing. * Debugging complex stateful or event-driven systems. * Logging, monitoring, observability, and production incident investigation. You should be comfortable working in an existing codebase where understanding current behaviour before changing it is essential. Highly Desirable Experience in one or more of the following would be particularly valuable: * Algorithmic or systematic trading systems. * Interactive Brokers / IBKR APIs. * Order management and execution systems. * Quantitative research infrastructure. * Backtesting and market simulation. * Market-data pipelines. * Time-series data. * Financial markets and order lifecycle concepts. * Deterministic or event-driven systems. * Cloud or containerised deployment. * CI/CD and automated release processes. Direct quantitative finance experience is valuable but not mandatory if you have strong engineering fundamentals and can understand complex domain logic. How We Work ZEUS has been developed around several important engineering principles: Determinism — the same inputs and system state should produce the same decisions. Reproducibility — research findings must be traceable to exact data, parameters, code, and strategy versions. Auditability — important decisions and lifecycle events must leave durable evidence. Production parity — research and simulation must accurately represent the behaviour of the live system. Safe evolution — changes must be understood, tested, and validated before affecting production behaviour. We value engineers who investigate before rewriting, understand systems before abstracting them, and treat correctness as more important than cleverness. What Success Looks Like Within your first few months, you should be able to: * Understand the architecture and critical execution paths of ZEUS. * Independently diagnose and resolve platform issues. * Implement clearly specified platform and research changes safely. * Improve automated testing and operational reliability. * Reduce the founder’s involvement in routine software engineering and platform maintenance. * Become a trusted technical contributor capable of evolving the platform without compromising its behavioural integrity. Engagement We expect to begin with a part-time or contract engagement, with the potential to expand as the platform and business grow. The role is remote and suited to someone comfortable working with a founder-led, technically complex platform where autonomy comes with a high standard of engineering discipline. When applying, please include: * A short description of the most technically complex Python system you have built or maintained. * Any experience with trading, financial systems, event-driven platforms, or data-intensive systems. * An example of a difficult production bug or systems issue you diagnosed and how you approached it. * Your experience using AI-assisted software development tools. * Your availability and preferred engagement model.