MLOps Platform Engineer

MLOps Platform EngineerPhoenix, Arizona

Not sure what skills you will need for this opportunity Simply read the full description below to get a complete picture of candidate requirements.
This is a unique opportunity to build an enterprise-scale MLOps platform from the ground up. Joining a greenfield initiative, you will play a pivotal role in designing and operationalizing the infrastructure, automation, and governance required to deploy, monitor, and scale machine learning models in production. This project offers the chance to shape long-term MLOps strategy while working closely with Data Science and Data Engineering teams to establish best practices across the organization.
The CompanyThey are a large, established organization undergoing significant investment in data, analytics, and machine learning capabilities. With a strong focus on innovation and operational excellence, they are building modern data and AI platforms to support critical business initiatives. Their teams are committed to creating scalable, governed, and sustainable solutions that enable advanced analytics and machine learning at enterprise scale.
The Role and DeliverablesDesign and build a production-grade MLOps platform using Snowflake and related machine learning capabilities.Develop reusable ML pipelines covering training, validation, deployment, inference, monitoring, and lifecycle management.Establish model governance standards including versioning, approval workflows, lineage tracking, rollback procedures, and auditability.Implement model observability frameworks for performance monitoring, drift detection, data quality validation, and service reliability.Build and automate CI/CD processes for machine learning workflows, including testing, release management, and deployment controls.Partner closely with Data Scientists and Data Engineers to productionize models and ensure reliable integration with enterprise data platforms.
Your Skills & ExperienceStrong experience building MLOps platforms or machine learning infrastructure in production environments.Proven capability designing and implementing model deployment, monitoring, tracking, versioning, and registry processes.Advanced Python and SQL skills with experience developing and maintaining machine learning pipelines.Experience building CI/CD pipelines and automation frameworks for machine learning systems.Hands-on experience with enterprise machine learning platforms such as Snowflake ML, Databricks, AWS SageMaker, or similar technologies.Strong understanding of the relationship between data engineering pipelines and downstream machine learning workflows.Experience working with cloud platforms, ideally AWS.Ability to collaborate effectively with Data Scientists and Data Engineers to define requirements and deliver scalable solutions.Familiarity with Snowpark, Feature Stores, Model Registry capabilities, or other modern MLOps tooling is advantageous. xzbcwsv Experience with medallion architectures, ML observability, AI agents, containers, dbt, Splunk, or near real-time machine learning environments would be beneficial.