Full-Time Senior Machine Learning Engineer
Experian is hiring a remote Full-Time Senior Machine Learning Engineer. The career level for this job opening is Senior Manager and is accepting São Paulo, Brazil based applicants remotely. Read complete job description before applying.
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We are seeking a Senior Machine Learning Engineer to join our Data Platform & MLOps team. Our mission is to develop tools and processes that drive innovation and optimize the operation of Machine Learning projects across the company. The MLE squad plays a central role in building the MLOps platform, focusing on creating new functionalities encompassing the entire model development and delivery lifecycle, from data collection to production deployment. Here, you will have the opportunity to make a direct impact on different areas of the company, working with cutting-edge technology and modern frameworks.
We offer a collaborative and dynamic environment where you will have the freedom to explore and innovate, with the support of a highly qualified and open-minded team.
Responsibilities:
- Collaborate with a multidisciplinary and agile team of Machine Learning Engineers, Data Engineers, and Site Reliability Engineers to architect and implement ML solutions, whether tools, services, or contributions to our ML and Data platform.
- Develop tools to assist our Data Scientists in their daily tasks.
- Develop robust and scalable architectures, always focusing on MLOps concepts.
Requirements:
- Previous experience as a Machine Learning Engineer.
- Experience with Git.
- Experience with cloud (AWS).
- Experience with CI/CD (Jenkins, ArgoCD, Harness).
- Experience with model development.
- Basic knowledge of Software Engineering.
- Solid knowledge of ML fundamentals: not only algorithms but also how they work.
- Advanced knowledge in Python.
- Advanced knowledge of data manipulation tools (Spark, Pandas, NumPy).
- Basic knowledge of containerization technologies (Docker, Podman).
- Advanced knowledge of ML libraries (scikit-learn, TensorFlow, PyTorch).
- Understanding of the MLOps culture and its importance.
Desirable:
- Experience with Kubernetes.
- Knowledge of ML project workflow tools (DVC, MLflow).