Principal Machine Learning Engineer
Core
Architect and lead the full lifecycle of ML systems for a new matching platform, focusing on scalable data pipelines, model governance, explainability, and MLOps automation.
Role type
Principal Machine Learning Engineer (MLOps, Model Governance, Explainability)
Builds
Production ML systems, data pipelines, inference runtimes, and governance frameworks for a matching platform
Domain
Financial markets infrastructure, matching platforms, enterprise ML
Deliverable
production ML models | infrastructure
Required skills
AWS SageMaker, Python, PyTorch, TensorFlow, XGBoost, MLOps automation, model governance, explainability (SHAP), low-latency inference, drift detection, CI/CD for ML
Preferred skills
Lakehouse architecture, feature stores, cross-account IAM patterns, shadow-mode testing, A/B testing strategies
Technologies
AWS SageMaker, PyTorch, TensorFlow, XGBoost, SHAP
Responsibilities
Define end-to-end ML architecture including data pipelines and inference runtimes; Lead adoption of MLOps patterns and AWS SageMaker capabilities; Architect scalable feature pipelines within Lakehouse environments; Design ranking, scoring, and similarity models; Establish explainability standards and regulator-ready reason codes; Architect automated training, deployment, and retraining pipelines; Design low-latency, high-throughput inference services; Define observability standards for feature and concept drift; Enforce ML-specific security standards and governance frameworks; Lead validation strategies using golden datasets and benchmark suites.
Seniority
Principal, hands-on IC with strategic leadership
