Senior MLOps Engineer

Role: Senior MLOps Engineer Contract Length: through the end of the year, may be potential for extension
Start date: ASAP
Location: Hybrid Phoenix Preferred (Remote Considered)
Position Overview
We are seeking a Senior MLOps Engineer for our client to help design, build, and scale a new enterprise machine learning operations platform. This is a unique opportunity to establish foundational MLOps capabilities from the ground up, creating the frameworks, standards, and automation required to support production machine learning workloads at scale.
The ideal candidate will be a hands-on builder with expertise in machine learning operations, CI/CD, model lifecycle management, and cloud-native data platforms. This role will work closely with Data Science and Data Engineering teams to transform experimental models into reliable, governed, and production-ready services.
The platform is being developed on a modern data architecture utilizing curated data layers and governed data products to support scalable, repeatable model development and deployment.
Key Responsibilities

• Design and implement a scalable MLOps platform supporting enterprise machine learning initiatives.
• Build reusable frameworks and pipelines for model training, validation, deployment, inference, monitoring, and maintenance.
• Develop workflows that ensure machine learning solutions consume trusted, governed, and production-ready data assets.
• Establish model lifecycle management practices, including versioning, approval processes, promotion workflows, rollback mechanisms, and lineage tracking.
• Partner with Data Scientists to operationalize machine learning models and accelerate the transition from experimentation to production.
• Implement monitoring and observability capabilities for model performance, drift detection, data quality, bias monitoring, and service reliability.
• Automate model retraining and refresh processes using orchestration and workflow automation technologies.
• Collaborate with Data Engineering teams to ensure feature engineering pipelines are reliable, reusable, and aligned with evolving data architecture standards.
• Define and implement CI/CD standards for machine learning assets, including code, data, configurations, and models.
• Establish governance practices supporting security, compliance, auditability, reproducibility, and responsible AI initiatives.
• Create operational documentation, technical standards, runbooks, and developer enablement materials.
• Help mature the platform from an initial framework into a scalable enterprise capability.

Required Qualifications

• 5+ years of experience in Machine Learning Engineering, MLOps, Platform Engineering, or a similar technical discipline.
• Strong development experience with Python and SQL.
• Demonstrated success building and supporting production machine learning pipelines.
• Experience with model deployment, versioning, governance, monitoring, retraining, drift detection, and lifecycle management.
• Experience implementing CI/CD pipelines and automated testing frameworks for machine learning environments.
• Knowledge of feature engineering processes, training-serving consistency, and data quality controls.
• Experience working with cloud platforms and related services; AWS experience is highly desirable.
• Strong communication and stakeholder engagement skills with the ability to collaborate across Data Science, Data Engineering, and business teams.
• Ability to explain, justify, and influence technical architecture and platform decisions.

Preferred Qualifications

• Experience with Snowflake-based machine learning capabilities, including Snowpark, Model Registry, Feature Store, or similar native ML tooling.
• Experience designing solutions within medallion, lakehouse, or layered data architectures.
• Knowledge of GitHub Actions and YAML-based CI/CD workflows.
• Experience with orchestration and transformation tools such as Airflow, dbt, or comparable technologies.
• Familiarity with real-time or near real-time inference architectures.
• Experience building greenfield platforms and establishing operational standards from the ground up.
• Background supporting large-scale operational environments such as logistics, transportation, fleet management, route optimization, manufacturing, utilities, or other high-volume industries.