Senior MLOps Engineer / MLOps Architect

Responsibilities

• Develop and maintain production-ready machine learning models using Python, C++, and TensorFlow.
• Integrate and deploy ML models into the company's trading infrastructure.
• Automate the model development, training, and deployment processes using tools like MLFlow.
• Monitor model performance, identify issues, and implement mechanisms for model updating and retraining.
• Collaborate with quant researchers and trading teams to ensure the seamless integration of ML models into the trading workflow.
• Optimize model performance and efficiency to meet high-frequency trading requirements.
• Continuously improve the MLOps pipeline and infrastructure to enhance model reliability, scalability, and reproducibility.
• Troubleshoot and resolve issues related to the deployment and operation of ML models.
• Document and maintain the MLOps processes and best practices.

We expect

• A bachelor's or Master's degree in Computer Science, Data Science, or a related field.
• 4+ years of working experience as a MLOps Engineer, ML Engineer, ML Architect, or similar.
• Extensive experience in Python or C++ programming, with a strong understanding of software engineering best practices.
• Proficient in TensorFlow and other popular machine learning frameworks.
• Hands-on experience with MLFlow or similar ML lifecycle management tools.
• Familiarity with cloud computing platforms (AWS) and containerization technologies (Docker, Kubernetes).
• Strong problem-solving and critical thinking skills to analyse and optimize ML models.
• Excellent communication and collaboration skills to work effectively with cross-functional teams.
• English proficiency at the B2 level or higher.

Nice to have:

• Experience in high-frequency trading or financial services is preferred, but not mandatory.

What we offer

• Working in a modern international technology company without bureaucracy, legacy systems, or technical debt.
• Excellent opportunities for professional growth and self-realization.
• Remote work from anywhere in the world with a flexible schedule.
• Compensation for health insurance, sports activities, and non-professional training.