MLOps Engineer

23 hours ago

Remote, WorldwideSenior

Responsibilities

Design, implement, and maintain MLOps workflows for model training, deployment, monitoring, retraining, and retirement.
Productionize data science assets as modular, testable, deployable Python packages and services.
Build and manage CI/CD pipelines for machine learning models, feature pipelines, and data products.
Implement MLflow experiment tracking, model registry, approvals, versioning, and rollback.
Develop feature engineering pipelines and reusable feature assets with 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 monitoring and observability for service health, latency, model performance, drift, and operational reliability.
Ensure governance, security, lineage, auditability, and access controls through Unity Catalog and Azure security services.
Optimize ML platforms for scalability, reliability, performance, and cloud cost efficiency.
Define and promote MLOps practices, engineering standards, and platform architecture across teams.

Deep expertise in Azure Databricks, MLflow, Databricks Feature Engineering, Azure Machine Learning, PySpark, containerization, and cloud-native software engineering.
Experience with production machine learning systems, model lifecycle management, deployment automation, platform engineering, and observability.
Experience partnering with Data Scientists and Data Engineers to operationalize machine learning solutions.
Ability to build secure, reliable, scalable, and compliant ML platforms.
Experience with Kubernetes-based inference platforms and automated testing and release processes.