Night-Shift Quantitative DevOps for Trading Systems

Radix Trading Experienced Job Board

Chicago (IL)

On-site

USD 110,000 - 170,000

Full time

14 days+

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Job summary

Radix Trading is seeking a DevOps Engineer to enhance live trading systems, research infrastructure, and alpha generation through technology innovation. You will cover our night shift in our Chicago office alongside engineers and researchers, focusing on Linux-based problem solving and production responsibilities.

You will work with cutting-edge software and hardware to push production-level code daily and to evolve automated strategies, live trading systems, and ML research infrastructure used

Qualifications

  • Strong Python proficiency with attention to language nuances and problem solving.
  • Persistent drive to improve and rise to the next level.
  • Creative, data-driven approach to testing new ideas.
  • Team-oriented mindset and collaboration to improve the whole team.
  • Adaptability based on new data, results, and feedback.
  • Entrepreneurial focus on maximizing value for the firm.

Responsibilities

  • Support live trading systems and research infrastructure on a nightly shift.
  • Translate research into production-ready solutions for trading machines.
  • Contribute to alpha generation through technology and automation.
  • Develop and maintain scalable distributed systems and architecture.
  • Collaborate across engineering and research to push production code daily.

Skills

Python
Analytical thinking
Team mindset
Creative problem solving
Mental flexibility
Orientation for making money

Job description

Radix Trading is seeking a DevOps Engineer to enhance live trading systems, research infrastructure, and alpha generation through technology innovation. You will cover our night shift in our Chicago office alongside engineers and researchers, focusing on Linux-based problem solving and production responsibilities.

You will work with cutting-edge software and hardware to push production-level code daily and to evolve automated strategies, live trading systems, and ML research infrastructure used

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