MLOps Platform Engineer - Build Production ML Infra

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

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