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Company Overview
A leading financial technology organization is seeking a Lead Machine Learning Engineer to join their Payments Intelligence (PINT) team. The organization focuses on building scalable, resilient data platforms that enable actionable insights from transaction data. This team powers customer-facing applications, agent tools, and machine learning models for fraud detection, subscription management, and enhanced transaction insights, supporting innovation in real-time financial services.
Summary
The Lead Machine Learning Engineer will design, develop, and deploy machine learning models and infrastructure at scale. This role combines ML architectural design, software engineering, and data platform expertise, with opportunities to implement innovative solutions that directly impact customer experiences.
Responsibilities
Design, build, and deploy machine learning models and components to solve real-world business problems
Inform ML infrastructure decisions through expertise in modeling techniques, feature selection, training, hyperparameter tuning, and model validation
Write, test, and maintain application code, ML models, and automated deployment pipelines
Collaborate with cross-functional Agile teams to enhance big data and ML applications
Retrain, maintain, and monitor models in production for high performance
Build cloud-based architectures and optimize ML models for scale
Construct and maintain data pipelines feeding ML models
Follow continuous integration and deployment best practices, including automated testing and monitoring
Ensure all code is well-managed, models are governed, and ML follows Responsible and Explainable AI best practices
Use programming languages such as Python, Scala, or Java
Basic Qualifications
Bachelor’s degree in Computer Science, Electrical Engineering, Mathematics, or a related field
6+ years designing and building data-intensive solutions using distributed computing
4+ years programming in Python, Scala, or Java
2+ years building, scaling, and optimizing ML systems
Preferred Qualifications
Master’s or Doctoral degree in a relevant field
3+ years building production-ready data pipelines for ML models
3+ years experience with ML frameworks such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
2+ years developing performant, resilient, and maintainable code
2+ years data gathering and preparation for ML models
2+ years people leadership experience
1+ years leading teams developing ML solutions using industry best practices and automation
Experience deploying ML solutions in public cloud environments (AWS, Azure, GCP)
Experience designing and scaling complex data pipelines and evaluating performance
Demonstrated ML industry impact through publications, open source contributions, or patents
Experience leveraging interactive AI tools to accelerate productivity beyond basic code completion
Disclaimer
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