Data Engineer (ML/AI) - Risk Data
Summary
Build and maintain ML infrastructure for credit-risk systems, deploying models, optimizing pipelines, and improving data quality and observability.
About The Team
We are looking for a Data / Machine Learning Engineer to join our team, building data and machine learning infrastructure for Credit Risk systems.
In this role, you will work closely with Data Scientists, Risk Policy, and Engineering teams to help productionize machine learning models, maintain large-scale data pipelines, and support real-time decision systems.
You will also have the opportunity to explore how AI technologies can improve model monitoring, data quality, and developer productivity across the risk team.
This role is ideal for engineers who enjoy working at the intersection of data engineering, machine learning systems, and platform infrastructure.
Job Description
Model Deployment & Inference
Support the deployment of machine learning models for real-time and batch risk decision systems Help build and maintain infrastructure for model serving and distributed inference Assist in optimizing model performance, latency, and system reliability
Data Pipeline & Feature Engineering
Build and maintain data pipelines supporting model training and inference Work with data scientists to ensure feature consistency between offline and online environments Develop and improve ETL / ELT workflows for large-scale data processing
Monitoring & Observability
Help build monitoring systems for data quality, feature drift, and model performance Investigate pipeline failures and assist in troubleshooting production issues Improve observability across data pipelines and model services
AI-assisted Platform Improvements
Explore ways to use AI to improve engineering workflows, such as: pipeline diagnostics data quality validation model monitoring analysis developer productivity tools
Collaboration
Work closely with Data Scientists, Risk Policy, and Engineering teams Support ML systems powering risk decisioning across multiple markets
Requirements
Bachelor's degree in Computer Science, Software Engineering, Data Engineering, Data Science, Artificial Intelligence, Machine Learning, Computer Engineering, Statistics, Applied Mathematics, or a related field. 3+ years of experience in Data Engineering, Machine Learning Engineering, or a related field Experience building data pipelines or ML systems in production Strong programming skills in Python / Spark Experience with large-scale data processing frameworks (Spark, Flink, etc.) Experience with workflow orchestration tools (Airflow or similar) Familiar with machine learning workflows and model deployment Understanding of distributed systems and data infrastructure Comfortable working with large-scale datasets and production systems
We are looking for a Data / Machine Learning Engineer to join our team, building data and machine learning infrastructure for Credit Risk systems.
In this role, you will work closely with Data Scientists, Risk Policy, and Engineering teams to help productionize machine learning models, maintain large-scale data pipelines, and support real-time decision systems.
You will also have the opportunity to explore how AI technologies can improve model monitoring, data quality, and developer productivity across the risk team.
This role is ideal for engineers who enjoy working at the intersection of data engineering, machine learning systems, and platform infrastructure.
Job Description
Model Deployment & Inference
Support the deployment of machine learning models for real-time and batch risk decision systems Help build and maintain infrastructure for model serving and distributed inference Assist in optimizing model performance, latency, and system reliability
Data Pipeline & Feature Engineering
Build and maintain data pipelines supporting model training and inference Work with data scientists to ensure feature consistency between offline and online environments Develop and improve ETL / ELT workflows for large-scale data processing
Monitoring & Observability
Help build monitoring systems for data quality, feature drift, and model performance Investigate pipeline failures and assist in troubleshooting production issues Improve observability across data pipelines and model services
AI-assisted Platform Improvements
Explore ways to use AI to improve engineering workflows, such as: pipeline diagnostics data quality validation model monitoring analysis developer productivity tools
Collaboration
Work closely with Data Scientists, Risk Policy, and Engineering teams Support ML systems powering risk decisioning across multiple markets
Requirements
Bachelor's degree in Computer Science, Software Engineering, Data Engineering, Data Science, Artificial Intelligence, Machine Learning, Computer Engineering, Statistics, Applied Mathematics, or a related field. 3+ years of experience in Data Engineering, Machine Learning Engineering, or a related field Experience building data pipelines or ML systems in production Strong programming skills in Python / Spark Experience with large-scale data processing frameworks (Spark, Flink, etc.) Experience with workflow orchestration tools (Airflow or similar) Familiar with machine learning workflows and model deployment Understanding of distributed systems and data infrastructure Comfortable working with large-scale datasets and production systems