The Business Intelligence Analyst will design and maintain PowerBI dashboards to provide actionable insights for strategic decision-making. They will collaborate with cross-functional teams to integrate data from various sources and optimize data models for cybersecurity-focused reporting.
The administrator will manage and maintain Red Hat Enterprise Linux and OpenShift container platforms, including hardware health and cluster operations. They will also support IBM Watson deployments and ensure system security, performance, and disaster recovery compliance.
The administrator will manage and maintain Red Hat Enterprise Linux and OpenShift container platforms, including hardware health and cluster operations. They will also support IBM Watson deployments, handle storage configurations, and ensure system security and compliance.
Convert data science prototypes into reproducible, production-quality ML services and manage large-scale data pipelines. Oversee the full model lifecycle, including tracking, CI/CD, automated retraining, and drift monitoring.
You will convert data science prototypes into production-grade ML services and manage large-scale data pipelines. Additionally, you will own the full model lifecycle, including MLOps, drift monitoring, and integration with advertising activation endpoints.
Design, automate, and maintain end-to-end MLOps workflows for model training, deployment, and monitoring on Azure. Partner with data science and engineering teams to operationalize machine learning solutions and establish robust MLOps standards.
The Sr. UI/UX Designer will own the end-to-end design of a product area, collaborating with product and engineering teams to connect user needs with business goals. Responsibilities include creating clickable prototypes, conducting usability research, maintaining design systems, and iterating on products post-release.
You will architect, build, and deploy high-performance machine learning systems while managing the entire ML lifecycle from data processing to production. Responsibilities include scaling feature pipelines, developing deep learning models, and maintaining robust MLOps infrastructure.
