The RoleWe are seeking a highly experienced software engineer to lead the design and development of next-generation trading systems. This is a hands-on technical leadership role focused on building scalable, resilient, and high-performance trading infrastructure. You'll collaborate across teams, mentor engineers, and drive innovation in a mission-critical environment.Design, develop, and optimize KDB+ databases and q analytics for high-volume trading and market dataDevelop Python-based AI and quantitative models for research, prediction, classification, and signal generationApply machine learning techniques to time-series data (feature engineering, model training, evaluation)Build research and backtesting frameworks integrating AI models with historical dataTranslate quantitative and ML research into robust, production-ready systemsIntegrate AI models into real-time and batch pipelinesOptimize analytics and model evaluation for performance, stability, and scalabilityCollaborate with quants, product owners, and engineering teams on model deployment and monitoringThe Expertise You Have:Bachelor's degree in Mathematics, Computer Science, Engineering, Information Technology, or equivalent.10+ years professional experience in quantitative finance or trading systemsAdvanced proficiency in KDB+/q, including:Time-series data modelingHigh-performance querying and joinsReal-time and historical analyticsStrong Python skills for:Quantitative analysisAI / ML model developmentIntegration with KDB+ and downstream systemsExperience working with large-scale, high-frequency, or noisy datasetsSolid software engineering practices (Git, testing, modular design)Worked with AI developer assist tools (e.g.
GitHub Copilot).Experience with CI/CD tools such as GitHub, Maven, Jenkins, Artifactory, and uDeploy.Hands-on experience in AWS or other cloud platforms.Familiarity with object-oriented programming languages such as JavaExperience in Linux, shell scripting, and production support experienceThe Skills You Bring:A strong quantitative mindset with practical AI application skillsAbility to bridge research, machine learning, and production systemsComfort working on front-office or research-critical infrastructureClear communicator with quants, traders, and engineersWillingness to support production systems and participate in on-call rotations, including occasional weekend support.