Full-Time Pessoa Engenheira de Machine Learning Sênior
Experian is hiring a remote Full-Time Pessoa Engenheira de Machine Learning Sênior. 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 looking for 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 directly impact different areas of the company, working with cutting-edge technology and modern frameworks. We offer a collaborative and dynamic environment where you will have freedom to explore and innovate, with the support of a highly qualified team open to new ideas.
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 that assist our Data Scientists in their daily tasks.
- Develop robust and scalable architectures, always focusing on MLOps concepts.
What we are looking for:
- Previous experience in Machine Learning Engineering
- Knowledge of Git
- Experience with cloud (AWS)
- Experience with CI/CD (Jenkins, ArgoCD, Harness)
- Knowledge in developing ML models
- Basic knowledge of Software Engineering
- Knowledge of ML fundamentals: Not only about algorithms, but how they function
- Advanced knowledge in Python
- Knowledge in data manipulation tools (spark, pandas, numpy)
- Basic knowledge of containerization technologies (Docker, Podman)
- Knowledge of ML libraries (scikit-learn, tensorflow, pytorch)
- Understanding of the MLOps culture and its importance
- Curiosity, adaptability, collaboration, ownership, and communication
Desired:
- Experience with Kubernetes
- Knowledge of ML project workflow tools (DVC, mlflow)