Machine Learning Operations Engineer

Panera Bread

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

Posted 10/8/2026full-timeUnited StatesMid-LevelSenior💰 $127,461 - $155,477 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in MLOps and Machine Learning Engineering, with a strong focus on deploying and maintaining production ML models. Proficient in utilizing Google Cloud Platform tools, including Vertex AI and BigQuery, while implementing CI/CD pipelines and model monitoring practices.

Highest-signal resume keywords

MLOps ExperienceMachine Learning Model DeploymentGoogle Cloud Platform ProficiencyCI/CD Pipeline DevelopmentTime Series Forecasting Expertise

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Python ProgrammingModel Evaluation MetricsForecasting TechniquesSQL ProficiencyData Pipeline ConceptsContainerization (Docker, Kubernetes)Infrastructure-as-Code (Terraform)Workflow Orchestration (Airflow, Cloud Composer)Statistical LibrariesAutomated Alerting Implementation

Soft Skills

Structured Problem-SolvingExcellent Communication Skills

Tools & Technologies

Vertex AIBigQueryCI/CD ToolsModel Monitoring ToolsFeature Stores

Industry Keywords

Sales ForecastingDemand ForecastingRetail OperationsMulti-Unit OperationsResponsible AI

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 models developed by data scientists and vendor partners
Own maintenance and performance of enterprise sales forecasting models
Diagnose forecast accuracy degradation and improve features, algorithms, hyperparameters, and model logic
Evaluate forecast performance using measures such as WMAPE and report results to business 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, Security, and Privacy teams
Deploy and manage models on Vertex AI, including training jobs, endpoints, batch prediction, and pipeline orchestration
Operationalize LLM and generative AI workloads, including BigQuery model invocation, prompt and model version management, and output validation
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 and environment promotion
Support data classification, access control, retention, governance, and audit requirements
Troubleshoot production model and pipeline failures, perform root cause analysis, and implement preventive controls
Define upstream data requirements and ensure model-input pipeline reliability
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 and applying software engineering practices to 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 expertise includes operationalizing LLMs, LLM-specific operational concerns, sales or demand forecasting in multi-unit retail or restaurant environments, feature stores, external vendor model handovers, responsible AI, model risk management, AI governance, and restaurant, retail, or multi-unit operations data