Backend SDET

• Design, develop, and optimize KDB+ databases and q analytics for high?volume trading and market data
• Develop Python-based AI and quantitative models for research, prediction, classification, and signal generation
• Apply machine learning techniques to time?series data (feature engineering, model training, evaluation)
• Build research and backtesting frameworks integrating AI models with historical data
• Translate quantitative and ML research into robust, production-ready systems
• Integrate AI models into real-time and batch pipelines
• Optimize analytics and model evaluation for performance, stability, and scalability
• Collaborate with quants, product owners, and engineering teams on model deployment and monitoring

Experienced in Serenity Cucumber Framework, Selenium, Playwright or Cypress

Experienced in Core Java and Java technologies

Background in KDB+ or other time-series databases / vector databases

Familiar with Linux environments and Unix shell scripting

Experience with Capital Markets or Brokerage systems, especially order management

Solid understanding of the FIX protocol and FIX engines

Familiarity with AI developer assist tools (e.g. GitHub Copilot)

Experience with CI/CD tools such as GitHub, Maven, Jenkins, Artifactory, and uDeploy

Ability to understand sophisticated, end-to-end systems and identify performance, scalability, or resilience bottlenecks

skills:

trading systems,Maven,Artifactory,AI,CI/CD,Cucumber,Cypress,databases,feature engineering,FIX protocol,GitHub Copilot,GitHub,uDeploy,Core Java,Java technologies,Jenkins,KDB+,Linux environments,machine learning techniques,AI models,ML research,model evaluation,Playwright,Python-based,Selenium,Unix shell scripting,resilient,resilience,high-performance,Brokerage,Capital Markets,design and development,trading infrastructure,prediction,innovation,market data,mentor,model training,order management,quantitative models,scalability,technical leadership,time-series,trading,vector databases

Design, develop, and optimize KDB+ databases and q analytics for high?volume trading and market data

Develop Python-based AI and quantitative models for research, prediction, classification, and signal generation

Apply machine learning techniques to time?series data (feature engineering, model training, evaluation)

Build research and backtesting frameworks integrating AI models with historical data

Translate quantitative and ML research into robust, production-ready systems

Integrate AI models into real-time and batch pipelines

Optimize analytics and model evaluation for performance, stability, and scalability

Collaborate with quants, product owners, and engineering teams on model deployment and monitoring