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# ml engineer in fintech

- **Company:** Enfint
- **Location:** Greater London, UK
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Data Analysis, Artificial Neural Networks, Information Engineering, Data Infrastructure, Data Visualization, Python (Programming Language), Machine Learning, Natural Language Processing, Data Streaming, Support Vector Machine, Sql Optimization, Kaggle, Information Technology, Apache Kafka, Static Data, Data Pipelines
- **Published:** August 30, 2026
- **Apply:** https://www.collegerecruiter.com/job/2830498657-ml-engineer-in-fintech

## About the Role

* Hold a degree in STEM, such as Computer Science, Mathematics, Statistics, Physics, Chemistry, Electrical Engineering, or a related quantitative field
 * Apply strong mathematical and statistical fundamentals to complex data environments
 * Have hands-on experience across model training, evaluation, and deployment using Machine Learning, AI, Neural Networks, or NLP frameworks
 * Demonstrate strong proficiency in Python or Java for data scripting and production engineering
 * Have advanced SQL skills
 * Build static data pipelines and conduct deep-dive data analysis
 * Use data visualization tools to understand statistical behavior
 * Nice to have: success in competitive machine learning environments or platforms such as Kaggle, KDD competitions, or Google Summer of Code / GSoC
 * experience with Graph Neural Networks (GNNs), Support Vector Machines (SVM), Natural Language Processing (NLP), Transformers, or LSTMs
 * familiarity with real-time streaming data pipelines such as Kafka
 * domain experience in Fintech, E-commerce, or fast-scaling tech companies

## Description

* Build, scale, and maintain the integrity layer of the label platform for Risk ML models
 * Define, implement, and monitor statistical fundamentals and key quality metrics for data and labels
 * Design automated audit processes to evaluate and monitor label quality over time
 * Work end-to-end on machine learning model training, evaluation, and pipeline deployment
 * Collaborate closely with cross-functional partners across Risk Intelligence, Data Engineering, and Product


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