MLOps Engineer (Forecasting)

Location: Remote (USA)

Industry: Healthcare

About the Role: We are seeking an experienced MLOps Engineer with strong expertise in forecasting models, Azure Cloud, and Databricks to support a healthcare-focused analytics initiative. This role will be responsible for building, deploying, automating, and maintaining machine learning solutions while ensuring reliability, scalability, and operational excellence throughout the ML lifecycle. The ideal candidate will have hands-on experience with forecasting and time-series models, cloud-based ML deployments, MLOps best practices, and production support of machine learning systems.

Key Responsibilities: Design, develop, test, and deploy machine learning forecasting models. Build and maintain scalable data pipelines in collaboration with data engineering teams. Perform feature engineering and feature selection to improve model performance and accuracy. Monitor, evaluate, and optimize forecasting models using appropriate performance metrics. Implement MLOps best practices including CI/CD pipelines, model versioning, automation, and deployment frameworks. Manage production deployments, model monitoring, retraining processes, and operational support. Troubleshoot model performance issues and support ongoing maintenance activities. Document machine learning models, workflows, deployment processes, and operational procedures. Collaborate with cross-functional teams including Data Engineers, Data Scientists, and business stakeholders.

Required Qualifications: 3+ years of experience in MLOps, Machine Learning Engineering, or Data Science. Strong programming experience with Python and R.Hands-on experience with Azure Cloud services. Experience using Databricks for data processing, model development, and machine learning workflows. Strong understanding of forecasting techniques and time-series modeling. Experience building and managing CI/CD pipelines for machine learning deployments. Experience with model deployment, monitoring, automation, and production support. Familiarity with machine learning frameworks such as Scikit-Learn and TensorFlow.

Preferred Qualifications: Experience with MLflow, Azure Machine Learning, or similar MLOps platforms. Azure certifications. Experience deploying machine learning models in cloud environments. Exposure to advanced forecasting and predictive analytics techniques. Healthcare domain experience is a plus.