Frontdoor is looking to build and optimize a platform that supports data science and AI/ML initiatives, streamlining data pipelines, managing ML models, and automating IT operations using the Snowflake Data Cloud.
Requirements
- 5+ years of experience in data engineering, MLOps, AIOps, or AI Platform roles.
- Expertise in the Snowflake Data Cloud, including data warehousing, data engineering features, and ML capabilities using Cortex AI features
- Proficiency in Python and SQL for data manipulation, scripting, and automation.
- Experience with AWS cloud platforms and their data and AI/ML services.
- Experience with MLOps frameworks and tools (e.g., MLflow, Kubeflow, Snowflake Model Registry).
- Solid understanding of DevOps principles and CI/CD practices for data and AI/ML workflows.
- Experience with observability tools (monitoring, logging, alerting) and setting up automated incident response.
Responsibilities
- Architect, build, and maintain the AI platform infrastructure, using cloud-native services and Snowflake's capabilities to support the entire ML lifecycle.
- Implement and manage MLOps pipelines for model training, testing, deployment, and monitoring, ensuring smooth integration with Snowflake data sources.
- Apply machine learning techniques to automate data operations, including monitoring, anomaly detection, incident response, and performance optimization.
- Configure and manage model registries (e.g., Snowflake Model Registry, MLflow) for versioning, tracking, and governance of ML models.
- Implement CI/CD pipelines for automating the deployment and updates of ML models.
- Integrate AIOps platforms with existing application infrastructure, cloud services, and observability tools (e.g., Splunk), using Snowflake as a central data repository.
- Champion data governance and observability frameworks to uphold data integrity, compliance, and security across all platforms.
Other
- The Senior AI Platform Engineer is a technical lead responsible for building and optimizing the platform that supports data science and AI/ML initiatives.
- Lead the adoption of business-focused domain architectures, effectively marrying technology solutions with business goals.
- Master and apply various data modeling techniques, including dimensional modeling, with emphasis on Star and other related analytical modeling techniques.
- Engage with internal stakeholders to gain a deep understanding of data needs, delivering solutions that drive strategic business objectives.
- Offer leadership and mentorship to the data engineering team, cultivating a culture of innovation and continuous learning.
