Machine Learning Engineer

About The Team

We are seeking a skilled Machine Learning Engineer to join our Credit Risk systems team, focusing on developing data and machine learning infrastructure. This position involves close collaboration with Data Scientists, Risk Policy, and Engineering colleagues to operationalize machine learning models, maintain extensive data pipelines, and support systems making real-time decisions.

The role offers a chance to leverage AI technologies aimed at enhancing model supervision, data integrity, and boosting efficiency for developers within the risk management team. This opportunity suits candidates who thrive in roles intersecting data engineering, ML systems, and platform infrastructure, tasked with building dependable solutions for scalable risk decision-making.

Responsibilities

• Facilitate the deployment of ML models into both real-time and batch risk decision frameworks.
• Create and sustain infrastructure for serving models, distributed ML inference, and production ML pipelines.
• Develop and manage data pipelines at scale that enable model training, evaluation, and inference.
• Coordinate with Data Scientists to maintain feature uniformity from offline developments to live production environments.
• Enhance ETL/ELT processes handling large risk-related datasets.
• Construct and upgrade surveillance mechanisms monitoring data quality, feature shifts, model output, and pipeline health.
• Analyze and resolve pipeline failures, data mismatches, and operational issues within model and data ecosystems.
• Boost observability, reliability, and scalability of data pipelines and model services.
• Investigate AI applications for improving engineering practices, including diagnostics, data validation, monitoring analytics, and productivity tools for developers.
• Work in partnership with teams across Data Science, Risk Policy, Data Engineering, and Software Engineering to support ML systems used for risk assessment across various markets.

Requirements

• Bachelor's degree in Computer Science, Software/Data Engineering, Data Science, AI, ML, Computer Engineering, Statistics, Applied Math, or related disciplines.
• At least three years' experience in Data Engineering, ML Engineering, ML Platform Engineering, or related fields.
• Proven practical expertise in constructing data pipelines, machine learning systems, or production-grade data infrastructure.
• Proficiency in Python programming combined with experience using Spark.
• Experience working with extensive data processing frameworks like Spark, Flink, or equivalents.
• Familiarity with scheduling and workflow orchestration tools such as Airflow or similar platforms.
• Knowledge of machine learning lifecycle workflows, including deployment, inference, monitoring, and production support.
• Understanding of distributed systems, data infrastructure, and production system reliability practices.
• Comfortable managing large datasets and addressing production environment challenges.
• Effective communication abilities and aptitude for cross-team collaboration with Data Science, Risk, and Engineering groups.