Machine Learning Engineer

About the position

Quantix is seeking a Machine Learning Engineer to help design, deploy, and maintain the ML systems that power our predictive analytics, risk intelligence, and real-time market insights. You will work across model pipelines, data infrastructure, and production-grade ML workflow automation. This role blends engineering depth with applied machine learning, enabling you to ship high-impact systems used across our institutional asset management ecosystem.

Job responsibilities

• Build, optimize, and maintain end-to-end machine learning pipelines for training, validation, and deployment.
• Implement production-ready ML systems that power forecasting, classification, and anomaly detection models.
• Collaborate with AI researchers to convert experimental models into scalable, efficient production services.
• Develop automated workflows for data ingestion, preprocessing, feature engineering, and model monitoring.
• Improve model performance through iteration, experimentation, and continuous integration of new signals.
• Work with large-scale datasets (market data, on-chain data, alternative datasets) to enhance model robustness.
• Deploy and monitor models in real-time environments, ensuring reliability, accuracy, and low-latency performance.
• Contribute to internal tooling, documentation, and infrastructure enhancements across the ML ecosystem.

Required skills

• Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Experience building production ML pipelines, APIs, or model-serving workflows.
• Solid understanding of data engineering, feature pipelines, and model lifecycle management.
• Exposure to time-series modeling, forecasting techniques, or quantitative datasets (preferred but not required).
• Familiarity with cloud environments (AWS/GCP), containerization (Docker), and orchestration tools.
• Ability to write clean, efficient, well-structured code for reproducible ML workflows.
• Experience with monitoring tools, performance optimization, or real-time model deployment is a plus.
• Problem-solving mindset with the ability to work in a fast-moving, research-driven engineering environment.

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