Machine Learning Operations (MLOps) Engineer

What you'll do

• Build and maintain model CI/CD, deployment, monitoring, retraining, and lifecycle management workflows.
• Maintain and improve sales forecasting models by diagnosing accuracy degradation, recalibrating models, and reporting performance.
• Operationalize models on Vertex AI and LLM workloads with BigQuery, while addressing reliability, governance, security, and cost.

What they ask for

• 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.
• Experience deploying and operating production ML models, including owning and improving time series or demand forecasting models.
• Strong proficiency in Python; hands-on Google Cloud Platform experience, particularly Vertex AI and BigQuery.

Nice to have

• Hands-on experience operationalizing LLMs, including Gemini via Vertex AI or in-warehouse invocation from BigQuery.
• Experience with sales or demand forecasting in a multi-unit retail or restaurant environment.

Pay

$127,461 to $155,477 annually (base salary)

Tools

Python, Google Cloud Platform, Vertex AI, BigQuery, Docker, Kubernetes, Terraform, Airflow

AI focus

Productionize, monitor, and improve ML and sales forecasting models; operationalize LLM and generative AI workloads, including prompt and model version management and output validation.