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Cloud AI Operations Specialist
1 semana atrás
We are a business that empowers people in service organizations with innovative software solutions. Our mission is to deliver Enterprise and Business software that works for people.
Our approach focuses on self-driving, adaptive, and intuitive software that changes the way people work. We aim to empower people and provide a better work experience, allowing them to spend time on meaningful work they live for.
Job DescriptionThis role requires an expert in managing AI and machine learning models on Snowflake. The ideal candidate will take ownership of the operational lifecycle of AI/ML models deployed within Snowflake, ensuring their reliability, scalability, and performance.
The successful candidate will bridge the gap between development and production environments, applying advanced MLOps practices tailored to Snowflake's data ecosystem. This will enable seamless AI-powered insights for the business.
This role involves acting as the primary point of accountability for AI operations, collaborating with data scientists and engineers to ensure models are properly versioned, tested, and deployed.
Key Responsibilities- Model Deployment and Management:
- Develop and maintain deployment pipelines for AI/ML models, ensuring a seamless transition from development to production.
- Collaborate with data scientists and engineers to ensure models are properly versioned, tested, and deployed.
- Implement monitoring tools to track performance metrics like accuracy, latency, and resource utilization.
- Monitor AI systems in real-time to detect anomalies, failures, or performance degradation.
- Respond to incidents to minimize downtime and business impact.
- Develop and maintain dashboards for key AI system performance metrics.
- Analyze and improve model inference times and operational efficiency.
- Identify bottlenecks in data pipelines and recommend solutions to optimize throughput.
- Proactively manage cloud resources to balance cost and performance.
- Collaborate with data engineering teams to ensure the availability of high-quality data for AI systems.
- Implement processes for automated data validation, anomaly detection, and error correction.
- Manage data lineage and compliance requirements for AI-related workflows.
- Apply best practices to enhance AI model lifecycle management.
- Stay updated on emerging technologies and tools for AI operations.
- Provide feedback to improve model training, testing, and deployment workflows.