MLOps Engineer

Responsibilities

• Design, implement, and maintain end-to-end MLOps workflows for model training, deployment, monitoring, retraining, and retirement
• Productionize data science assets by converting notebooks and prototypes into modular, testable, deployable Python packages and services
• Build and manage CI/CD pipelines for machine learning models, feature pipelines, and data products
• Implement model lifecycle management using MLflow, including experiment tracking, model registry, approval workflows, versioning, and rollback
• Develop and maintain feature engineering pipelines and reusable feature assets using Databricks Feature Engineering and Delta Lake
• Deploy and operate batch, streaming, and real-time inference workloads using Databricks Model Serving, Azure Machine Learning, and Kubernetes-based platforms
• Establish automated testing, validation, and release processes for ML code, data, features, and models
• Implement model monitoring and observability for service health, latency, model performance, drift detection, and operational reliability
• Ensure governance, security, lineage, auditability, and access controls through Unity Catalog and Azure security services
• Optimize ML platforms and workloads for scalability, reliability, performance, and cloud cost efficiency
• Define and promote MLOps best practices, engineering standards, and platform architecture across teams

Requirements

• 3+ years of hands-on experience building and operating production MLOps pipelines using Azure Databricks and MLflow
• 3+ years of hands-on experience with production ML deployment and automation, including CI/CD, PySpark, Docker/Kubernetes, and Azure Machine Learning or Databricks Model Serving
• Experience primarily focused on MLOps/ML platform engineering and productionizing machine learning solutions

About the Company

Fusemachines is a leading AI strategy, talent, and education services provider on a mission to democratize AI.