ML Development Engineer; Remote

Title:

ML Development Engineer

Location:

Remote
Job Type: Contract (12 Months)
Compensation: $75.19 - $90.22/hr
Industry: Retail

Our client, a Fortune 50 retailer and leader in omnichannel commerce, is seeking a Development Engineer to support and modernize demand forecasting capabilities within its digital fulfillment organization. This team develops forecasting solutions that help optimize order volume planning, fulfillment capacity, and workforce utilization across multiple customer fulfillment channels. In this role, you will collaborate closely with data scientists, machine learning engineers, and platform teams to bridge the gap between research and production.

You will help scale forecasting platforms, build robust machine learning pipelines, and improve the reliability, performance, and efficiency of enterprise-scale forecasting systems that directly impact operational planning and customer experience. This position is primarily remote, with a preference for candidates located near the client's hub locations. Limited onsite attendance may be required for local candidates.

As a Development Engineer, you will play a key role in designing, implementing, and supporting machine learning and data engineering solutions that power large-scale forecasting platforms.

• Build, deploy, and maintain machine learning solutions in production environments.
• Develop and optimize time-series forecasting models and supporting data pipelines.
• Convert and scale data-processing workloads from pandas-based frameworks to PySpark and other distributed processing technologies.
• Design and implement ML pipelines using orchestration tools such as Kubeflow Pipelines, Vertex AI, or Airflow.
• Integrate data validation, model training, testing, and deployment processes into automated workflows.
• Optimize data pipelines and storage solutions for scalability, performance, and cost efficiency.
• Collaborate with data science teams to product ionize research and analytics solutions.
• Maintain and enhance existing Python applications and codebases following software engineering best practices.
• Build and support CI/CD processes for machine learning workflows and platform deployments.
• Implement cloud-native solutions leveraging Google Cloud Platform (GCP) services and containerized environments.
• Monitor system performance, troubleshoot production issues, and improve operational reliability.
• Contribute to architecture decisions that support enterprise-scale forecasting and fulfillment systems.

• Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field; or equivalent practical experience.
• Experience building and deploying machine learning models in production environments.
• Hands‑on experience with time-series forecasting methodologies such as Prophet, ARIMA, or similar frameworks.
• Strong understanding of model validation, experiment tracking, and hyperparameter tuning.
• Experience with feature engineering and feature store concepts.
• Proficiency in Python and software development best practices.
• Experience working with distributed data processing technologies such as Spark, Dask, Ray, or similar platforms.
• Demonstrated ability to transform and scale large datasets using PySpark.
• Experience designing and optimizing data pipelines for performance, reliability, and cost efficiency.
• Working knowledge of analytical data platforms such as Big Query, including dataset design, partitioning, clustering, and data validation.
• Experience building and maintaining machine learning pipelines using Kubeflow Pipelines (KFP), Vertex AI, Airflow, or similar orchestration platforms.
• Understanding of pipeline architecture, workflow orchestration, caching strategies, and configuration management.
• Experience using Git-based version control and structured change management processes.
• Familiarity with testing frameworks, dependency management tools, and code quality practices.
• Experience working with cloud platforms such as Google Cloud Platform (GCP), AWS, or Azure.
• Knowledge of containerization and orchestration technologies including Docker and Kubernetes.
• Experience implementing CI/CD pipelines and deployment automation.
• Understanding of secrets management and secure environment configuration.

• Experience with Ray for distributed machine learning training and inference.
• Exposure to Hadoop ecosystem technologies, including Hive, HDFS, or Spark on YARN.
• Knowledge of machine learning model…