Senior AI Engineer (Snowflake)

We are looking for a highly motivated Senior AI Engineer to join our team. Ideal candidate will have expertise in managing AI and machine learning models on Snowflake. In this standalone role, you will take ownership of the operational lifecycle of AI/ML models deployed within Snowflake, ensuring their reliability, scalability, and performance. You will bridge the gap between development and production, applying advanced MLOps practices tailored to Snowflake’s data ecosystem to deliver seamless AI-powered insights to the business.

You will act as the primary point of accountability for AI operations, bridging the gap between development and production environments. Your work will directly impact the efficiency and effectiveness of AI solutions, empowering business teams to make data-driven decisions with confidence.

Key Responsibilities

1. Model Deployment and Management

• Build and maintain deployment pipelines for AI/ML models and ensure 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.

2. System Monitoring and Incident Management

• 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.

3. Performance Optimization

• 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.

4. Data Management

• 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.

5. Continuous Improvement

• 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.