We are a global technology partner with nearly two decades of engineering excellence, specializing in custom software solutions and high-performing development teams. We empower international clients by engineering seamless, mission-critical digital products tailored to their strategic goals.

I. Key Responsibilities

• High-Tier Technical Support: Diagnose complex technical issues for global research teams, troubleshooting Python/C++ codebases and architecting scalable fixes.

• C++ Performance Engineering: Profile, debug, and optimize C++ applications and data pipelines for maximum throughput, low latency, and reliability.

• AI & LLM Integration: Embed modern AI/LLM solutions to automate data ingestion, technical support, documentation, and research workflows.

• Data Enrichment & Analytics: Convert raw, unstructured data into clean, structured datasets for quantitative analysis and financial modeling.

• Data Quality Frameworks: Build automated validation tools and monitoring frameworks to guarantee data accuracy and pipeline hygiene.

• Pattern & Signal Discovery: Apply ML/DL methodologies to analyze complex data behavior and partner with quantitative researchers to identify tradable signals.

• Workflow Automation: Develop custom internal tools to automate testing, CI/CD deployment, and system monitoring across the development lifecycle.

II. What You Bring

• 4+ years of hands-on experience in software engineering, data engineering, machine learning, or AI systems.

• Dual-stack fluency: Proven capability in building real-time systems and data pipelines using Python and C++.

• AI/ML Foundation: Solid theoretical and practical grasp of deep neural networks, machine learning algorithms, and modern AI paradigms.

• C++ Expertise: Deep proficiency in C++ profiling, memory optimization, STL, OOP, and root-cause debugging.

• Modern Tooling: Hands-on experience applying LLM/AI tools for code optimization, workflow automation, or analytical support.

• Tech Standards: Familiarity with Python environments (Jupyter, PEP 8 standards) and Linux/Unix-based software engineering best practices.

• Education: Bachelor’s degree in Computer Science, Applied Mathematics, Quantitative Finance, or a related STEM field.

Bonus Points (Nice to Have)

• Background in data engineering or quantitative research within finance/investment domains.

• Proficiency with Linux shell, Git workflows, and advanced profilers/debuggers.

• Hands-on experience in time-series modeling and statistical forecasting.

• Practical experience applying LLMs to enhance developer productivity or automated data enrichment.

• Experience tuning and maintaining low-latency C++ systems in production.