Machine Learning Operations Engineer

Panera Bread

. Design, build, and maintain CI/CD pipelines for model training, validation, deployment, and rollback .

Posted 10/1/2026full-timeUnited StatesMid-LevelSenior💰 $127,461 - $155,477 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and maintaining CI/CD pipelines for machine learning models, with a strong focus on operationalizing models using Google Cloud Platform tools like Vertex AI and BigQuery. Proficient in model evaluation, monitoring, and improvement, ensuring high performance and accuracy in production environments.

Highest-signal resume keywords

MLOpsMachine Learning EngineeringCI/CD Pipeline DevelopmentGoogle Cloud PlatformPython Programming

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Model DeploymentForecasting TechniquesSQL ProficiencyData Pipeline ConceptsModel MonitoringHyperparameter TuningStatistical LibrariesContainerizationInfrastructure-as-CodeWorkflow Orchestration

Soft Skills

Structured Problem-SolvingCommunication Skills

Tools & Technologies

Vertex AIBigQueryDockerKubernetesTerraformAirflowCloud Composer

Industry Keywords

Demand ForecastingRetail OperationsResponsible AIFeature StoresVendor Model Handover

Tech Stack

Tools & technologies

AirflowBigQueryCloudDockerGoogle Cloud PlatformKubernetesPythonSQLTerraform

About the role

Key responsibilities & impact

Design, build, and maintain CI/CD pipelines for model training, validation, deployment, and rollback
Productionize data scientist and vendor models into scalable, maintainable, reproducible pipelines
Own maintenance and performance of enterprise sales forecasting models, including accuracy tracking, recalibration, and continuous improvement
Diagnose forecast accuracy degradation and improve features, algorithms, hyperparameters, and model logic
Evaluate forecast performance using measures such as WMAPE and report trends to stakeholders
Translate promotions, seasonality, café openings and closures, and operational disruptions into model features and adjustments
Partner with Finance, Operations, Supply Chain, Data Science, Data Engineering, and Security teams
Deploy and manage models on Vertex AI, including training jobs, endpoints, batch prediction, and pipeline orchestration
Operationalize LLM and generative AI workloads using BigQuery and Vertex AI
Implement monitoring for accuracy, drift, data quality, latency, and failures with automated alerting
Build automated retraining workflows with promotion criteria and approval gates
Establish model registry, versioning, and lineage practices
Manage feature pipelines and prevent training-serving skew
Monitor and optimize compute and inference costs
Define and enforce infrastructure-as-code standards for ML environments
Support security, privacy, governance, and audit requirements
Troubleshoot production failures, conduct root cause analysis, and implement preventive controls
Contribute to architecture reviews and present ML platform designs and operational readiness
Mentor engineers and data scientists on production standards and deployment practices

Requirements

What you’ll need

Bachelor's degree in Computer Science, Engineering, Data Science, Analytics, or a related field
3+ years of experience in MLOps, Machine Learning Engineering, Data Engineering, or DevOps supporting production ML workloads
Demonstrated experience deploying and operating machine learning models in production, and maintaining and improving them once live
Hands-on data science capability, including independently modifying model features, algorithms, and hyperparameters
Experience owning and improving time series or demand forecasting models in production
Working knowledge of forecasting techniques and accuracy measures such as WMAPE
Proficiency with common ML and statistical libraries
Strong proficiency in Python, including packaging, testing, and production-quality code
Hands-on experience with Google Cloud Platform, particularly Vertex AI and BigQuery
Experience building CI/CD pipelines for ML workflows
Experience with containerization and orchestration such as Docker or Kubernetes
Experience with infrastructure-as-code tooling such as Terraform
Working knowledge of workflow orchestration tools such as Airflow, Cloud Composer, or Vertex AI Pipelines
Strong SQL proficiency and understanding of data pipeline and warehouse concepts
Experience implementing model monitoring, drift detection, and automated alerting
Understanding of model evaluation metrics and production model performance
Structured problem-solving skills and persistence in driving production issues to root cause
Excellent written and verbal communication skills
Preferred/bonus expertise includes LLM operationalization, forecasting in multi-unit retail or restaurant environments, feature stores, vendor model handover, responsible AI, and restaurant or retail operations data