MLOps Platform Engineer

Harnham; Phoenix (AZ); On-site; USD 140,000 - 190,000; Full time

Job summary

Harnham in Phoenix leads a greenfield initiative to design and operate an enterprise-scale MLOps platform from the ground up. You will architect the infrastructure, automation, and governance to deploy, monitor, and scale ML models in production, collaborating with Data Science and Data Engineering teams to establish best practices.

You will own end-to-end ML tooling, CI/CD for ML, model observability, and registry capabilities, applying Python/SQL skills and AWS-based cloud experience to

Qualifications

• Proven experience building MLOps platforms in production environments.
• Design and implement model deployment, monitoring, versioning, and registry processes.
• Advanced Python and SQL skills for ML pipelines.
• Experience with CI/CD pipelines and automation for ML systems.
• Hands-on with enterprise ML platforms (Snowflake ML, Databricks, AWS SageMaker).
• Strong understanding of data engineering relationships to ML workflows.

Responsibilities

• Design and build a production-grade MLOps platform using Snowflake and related ML capabilities.
• Develop reusable ML pipelines for training, validation, deployment, inference, and monitoring.
• Establish governance standards: versioning, approvals, lineage, rollback, auditability.
• Implement model observability for performance, drift, data quality, and reliability.
• Build and automate CI/CD processes for ML workflows, including testing and release controls.
• Partner with Data Scientists and Data Engineers to productionize models with enterprise data platforms.

Skills

MLOps

Python

SQL

CI/CD

AWS

Snowflake

Databricks

Model Registry

Observability

Collaboration

Tools

Snowflake ML

Databricks

AWS SageMaker

Snowpark

dbt

Splunk

Job description

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 Company

They 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 Deliverables

• Design 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 & Experience

• Strong 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.
• Experience with medallion architectures, ML observability, AI agents, containers, dbt, Splunk, or near real-time machine learning environments would be beneficial.