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Middle-office role supporting trading desks: monitor trading activity, perform daily PnL reconciliation, investigate breaks, resolve trade issues, escalate pricing/risk problems, and drive data-driven process improvements and automation.

Support Quantitative Risk Management by building and executing quantitative risk monitoring, designing and analyzing key risk metrics, applying statistical modeling to assess future risk and opportunities, contributing to cross-functional initiatives, and presenting findings to executive management.

Manage and improve trading operations and dataflows: own SOD/EOD reconciliations, pricing, settlements, PnL reporting, intraday monitoring, design alerts and reconciliations, streamline trade execution and position distribution, onboard traders/products, evaluate new markets, and collaborate with developers on databases and trader tools.

Lead design and delivery of production LLM-powered agent systems and firm-wide agent standards. Build reference agent apps, connect agents to internal data/APIs, establish security and governance for AI, adopt deployment and observability infrastructure, and partner with stakeholders to identify high-value use cases.

Operate and evolve an on-prem Kubernetes platform for latency-sensitive trading systems. Own cluster lifecycle, observability (metrics, logs, traces), CI/CD and GitOps, infrastructure automation, and platform onboarding/support for trading desks. Build tooling, runbooks, and scale platform operations.

Operate and evolve an on‑prem, latency‑sensitive Kubernetes platform and observability stack. Build and maintain CI/CD and GitOps workflows, infrastructure automation, and tooling. Onboard and support trading desks, write runbooks, tune alerts, scale metrics/logs/traces, and drive platform adoption and disaster recovery planning.

Seeking a VP/Director for Eurex-focused futures sales, responsible for revenue growth, client relationships, and market insights.

Analyze high-frequency limit orderbook and tick data to discover predictive signals; build and backtest quantitative models; develop and maintain data pipelines; apply statistical and tree-based machine learning methods; collaborate with researchers and traders to productionize and monitor live signals.

Own end-to-end quantitative research lifecycle for systematic trading: ingest and version tick and market data, build Python-based research infrastructure, develop microstructure and relative-value signals, backtest and validate strategies, collaborate with traders to deploy live signals, and continuously optimize execution and tooling.

Lead the firm's counterparty and credit risk function by evaluating clients, counterparties, and credit investments; develop and own a credit risk framework; recommend internal ratings; perform portfolio reviews; and co-lead the Credit Risk Committee presenting analytics and recommendations to senior management.