Build high-performance Python systems powering quantitative research, systematic trading, automation, analytics, and risk workflows, collaborating with traders, researchers, and engineers across a sophisticated technology environment.

What You'll Do:

Develop, test, deploy, and maintain high-performance Python applications.
Build scalable tools supporting systematic trading and quantitative research.
Develop automation, analytics, visualization, and workflow optimization solutions.
Enable researchers to run large-scale computations and backtesting.
Work closely with traders, quants, researchers, and engineering teams.
Own projects throughout the full software development lifecycle.
Optimize systems while balancing latency, throughput, scalability, and maintainability.
Apply modern automated testing, CI/CD, and daily deployment practices.'

Your Responsibilities Will Include:

Engineering Python-based platforms that take trading strategies from prototype to production.
Creating business-critical applications for risk management and operational workflows.
Developing research tooling for distributed computing and large-scale quantitative analysis.
Implementing reliable automation solutions that improve productivity and efficiency.
Gathering requirements and translating complex stakeholder needs into practical software.
Maintaining robust, scalable systems across Linux, Docker, and Kubernetes environments.
Working with technologies such asPandas, NumPy, SciPy, Celery, Dask, and Spark .
Continuously improving software performance, reliability, testing, and deployment processes.

Why Join Us:

Work at the intersection ofsoftware engineering, quantitative research, and systematic trading .
Solve technically challenging problems involving high-performance and distributed systems.
Collaborate directly with experienced traders, quants, researchers, and engineers.
Build technology that directly influences sophisticated trading and research workflows.
Gain exposure to modern cloud, containerization, automation, and data-processing technologies.
Enjoy an environment where engineers have genuine ownership from concept through production.

About You:

5+ years of professional software engineering experience with strong Python expertise.
Strong knowledge of numerical computing and data-processing technologies.
Experience with automated testing and CI/CD pipelines.
Familiarity with Docker, Kubernetes, and Linux.
Exposure to distributed computing and cloud-based data processing.
Excellent problem-solving skills with a proactive approach to technical challenges.
Strong communication and cross-functional collaboration abilities.
Curious, adaptable, and eager to learn quantitative finance and financial markets.

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