Machine Learning Engineer
Core
Design, develop, and deploy end-to-end machine learning pipelines to power critical decisions and shape the future of a consumer finance platform.
Role type
Machine Learning Engineer
Builds
Production ML systems, data pipelines, and inference services for a fintech platform
Domain
Consumer Finance / Fintech
Deliverable
production ML models
Required skills
Python, Scikit-learn, LightGBM, PyTorch, MLOps (MLflow, Kubeflow, SageMaker), Pandas, NumPy, SQL, NoSQL, Docker, Kubernetes, AWS, GCP, Azure
Preferred skills
Feature engineering, hyperparameter tuning, scalable inference techniques, real-time and batch inference pipelines, model monitoring, bias detection, explainability techniques
Technologies
MLflow, Kubeflow, SageMaker, Docker, Kubernetes, AWS, GCP, Azure, Pandas, NumPy, Scikit-learn, LightGBM, PyTorch
Responsibilities
Design and deploy end-to-end ML pipelines; Implement MLOps best practices including CI/CD, versioning, and monitoring; Optimize models using feature engineering and tuning; Work with structured and unstructured data; Collaborate with data engineers on high-performance pipelines; Deploy and manage models on cloud platforms; Maintain model performance via continuous monitoring and explainability
Seniority
Mid-level, hands-on IC