Forecasting ML Engineer — Production Pipelines

Dahl Consulting; Minneapolis (MN); Hybrid; USD 10,358,000 - 12,429,000; Full time

Job summary

Dahl Consulting seeks a Development Engineer to design, implement, and support large-scale forecasting platforms powering demand planning and fulfillment. You will work with data scientists, ML engineers, and platform teams to productionize research.

Responsibilities include building ML solutions in production, developing time-series models, and scaling data pipelines using PySpark, Kubeflow, Vertex AI, or Airflow within a hybrid Minneapolis environment.

Qualifications

• Bachelor's degree in a technical field or equivalent experience.
• Experience building and deploying ML models in production.
• Hands-on time-series forecasting methods (Prophet, ARIMA, etc.).
• Strong model validation, experiment tracking, and hyperparameter tuning.

Responsibilities

• Build, deploy, and maintain ML solutions in production.
• Develop and optimize time-series forecasting models and data pipelines.
• Scale data-processing workloads to PySpark and other distributed tech.
• Design ML pipelines with Kubeflow, Vertex AI, or Airflow.
• Automate workflows for validation, training, testing, and deployment.
• Optimize pipelines for performance, reliability, and cost.
• Collaborate with data science to productionize research.
• Maintain and enhance Python applications following best practices.
• Build and support CI/CD for ML workflows and deployments.
• Leverage GCP services and containerized environments.
• Monitor, troubleshoot, and improve system reliability.
• Contribute to enterprise-scale forecasting/fulfillment architecture.

Skills

Python

Distributed data processing

Time-series forecasting

PySpark

Kubeflow Pipelines

Vertex AI

Airflow

BigQuery

GCP

Docker & Kubernetes

Git & CI/CD

Model validation

Experiment tracking

Education

Bachelor's degree in CS/Engineering/Data Science or related field

Tools

Spark

Dask

Ray

Job description

Dahl Consulting seeks a Development Engineer to design, implement, and support large-scale forecasting platforms powering demand planning and fulfillment. You will work with data scientists, ML engineers, and platform teams to productionize research.

Responsibilities include building ML solutions in production, developing time-series models, and scaling data pipelines using PySpark, Kubeflow, Vertex AI, or Airflow within a hybrid Minneapolis environment.