Senior Machine Learning Engineer, Fraud Risk Modeling

What you'll do

• Lead the architecture and implementation of machine learning models to solve real-world business challenges.
• Design and develop scalable infrastructure for model training, automated hyperparameter tuning, and deployment pipelines.
• Write high-quality, production-grade code to turn machine learning models into deployable services and APIs.
• Debug and troubleshoot model performance issues to enhance reliability, speed, and efficiency in production.
• Manage the full model lifecycle, including monitoring, retraining, and versioning to meet business needs.
• Mentor junior engineers and lead technical decision-making processes regarding software engineering and model development.
• Collaborate with cross-functional teams to integrate machine learning models into production systems.
• Implement MLOps practices, including CI/CD pipelines, automated testing, and performance monitoring systems.

What we're looking for

• B.Sc. in Computer Science, Machine Learning, Engineering, or a related technical field.
• 6+ years of experience applying machine learning techniques including deep learning, reinforcement learning, and NLP in production.
• 6+ years of experience with open-source/cloud-agnostic components like Snowflake, Kafka, PostgreSQL, MongoDB, Spark, and Airflow.
• 6+ years of professional software development experience with at least two languages such as Java, C++, Python, or C#.
• 6+ years of experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• 4+ years of experience with cloud platforms (AWS, Azure, GCP), Docker, and Kubernetes.
• Expertise in MLOps practices, including CI/CD, automated testing, and deployment pipelines for ML models.
• Advanced degree (M.Sc., Ph.D.) in a related field (preferred).