• Minimum 4+ years of hands-on experience in software engineering, data engineering, machine learning, AI, or related technical domains.

• Practical experience with, and theoretical understanding of, deep neural networks, machine learning techniques, and modern AI approaches.

• Demonstrated ability to implement data science pipelines and real-time applications in Python and C++.

• Strong experience in debugging, troubleshooting, and improving C++ code, including performance analysis, root cause investigation, and code maintainability.

• Familiarity with AI/LLM tools and applications, including using large language models to support analysis, automation, technical troubleshooting, or workflow optimization.

• Proficiency with Python-based tools such as Jupyter Notebook and coding standards such as PEP 8.

• Proficiency with C++ tools and concepts such as STL, object-oriented programming, and debugging best practices.

• Strong academic background, with a minimum of a bachelor’s degree in a technical or quantitative field.

• Exceptional analytical and problem-solving abilities, with strong attention to detail.

• Strong software development skills, with the ability to convert broad use cases into reliable working solutions.

• Motivated by curiosity, continuous learning, and solving complex technical problems.

*** Nice to have

• Experience as a data scientist or data engineer in finance or investment-related environments.

• Experience with Linux/Unix shell, Git, and software debugging tools.

• Practical experience with time-series forecasting and analysis methods.

• Experience applying AI/LLM technologies in engineering, analytics, data enrichment, support automation, or developer productivity use cases.

• Experience optimizing or troubleshooting C++-based systems in production or research environments.