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Research and develop intraday to few-day quantitative alpha signals and systematic trading strategies for APAC equities and futures. Apply machine learning, high-performance computing, time-series modeling, and market microstructure analysis to create scalable trading models. Collaborate with traders, engineers, and researchers to deploy production-ready strategies and improve quantitative research methodologies, tools, and best practices.

Build and operate trading-critical infrastructure across Linux, on-premise, and multi-cloud environments. Develop reusable Terraform modules, GitHub Actions workflows, Kubernetes platforms, and internal automation in Python or Go. Manage GCP, AWS, Alibaba Cloud, Kafka, Linux provisioning, configuration, patching, and lifecycle operations. Participate in production incident response and a global 24/7 on-call rotation while enabling secure self-service infrastructure for Technology teams.

Defines long-term architecture and engineering standards across enterprise systems, including identity, endpoints, compute, virtualization, operating systems, storage, databases, and SaaS foundations. Leads modernization, cloud migration, automation, platform rationalization, and legacy system retirement. Acts as senior technical authority, mentors engineers, drives architecture decisions, and owns integrations such as SSO, provisioning, APIs, and data flows. The role is a hands-on individual contributor based in London with frequent travel to Amsterdam.

Designs, builds, optimizes, and deploys systematic trading models and algorithms. Analyzes large datasets to identify market patterns and trading signals, collaborates with traders and technologists to move research into production, and monitors model performance. Requires advanced quantitative expertise, strong mathematics and statistics, programming proficiency, and the ability to solve ambiguous problems independently.

Design and optimize low-latency C++ components for market data ingestion, normalization, distribution, order execution, and systematic trading infrastructure. Build tools for quantitative researchers and technical traders, and own components through deployment, monitoring, profiling, and production optimization. Collaborate with engineers, quants, and traders to develop highly scalable systems using modern C++, OS internals, CPU and memory architecture, lock-free concurrency, and low-latency networking.

Provide end-user technical support across Windows systems, office infrastructure, datacenter environments, and trading floors. Manage tickets, troubleshoot hardware and applications, configure workstations and meeting-room technology, provision access, support onboarding and offboarding, monitor systems, maintain asset inventories, respond to high-priority incidents, and document recurring issues. Collaborate with infrastructure, network, Linux, and SRE teams to deliver reliable IT solutions and meet SLA targets.

Lead engineering teams by owning roadmaps, delivery planning, hiring, performance management, career development, team culture, and cross-functional alignment. Partner with technical leads, Trading, and Quant teams to deliver software products, manage dependencies and risks, improve delivery predictability, and align engineering work with company goals.

Lead development and global rollout of a machine learning research framework for training, evaluating, combining, and deploying trading models. Partner with quantitative researchers to test hypotheses, develop systematic trading strategies, and create alpha signals. Build end-to-end machine learning pipelines covering data preprocessing, model training, production deployment, and monitoring while applying modern software engineering, cloud, containerization, and MLOps practices.

Design, build, deploy, and operate enterprise-grade AI agents and multi-agent workflows. Integrate agents with internal systems, implement RAG/vector DBs, ensure governance, observability, evaluation, and production guardrails, and deliver automation across Trading, Risk, Compliance, Finance, and Operations.

Develop quantitative trading strategies for digital asset markets, including pricing, execution, and systematic market-making models. Research and optimize strategies using large-scale market data, machine learning, and high-performance computing, then collaborate with Trading and Technology to deploy them into production. The role requires expertise in systematic trading, electronic markets, market microstructure, and translating quantitative methods into profitable trading outcomes.