Machine Learning / MLOps Engineer
Core
Build, deploy, and support production-ready machine learning solutions on Azure and Databricks, operationalizing models and managing the end-to-end ML lifecycle.
Role type
MLOps Engineer
Builds
Scalable data and ML pipelines, model deployment processes, and monitoring systems for production ML models.
Domain
Cloud infrastructure (Azure) and Data Engineering
Deliverable
production ML models
Required skills
Azure Cloud, Databricks, Python, PySpark, SQL, MLflow, CI/CD, Git, Machine Learning deployment, model monitoring, observability, testing, governance
Preferred skills
Generative AI / LLM development (LangChain, LangGraph, RAG), Unity Catalog, Databricks Model Registry, Azure DevOps, GitHub Actions, Docker, Kubernetes (AKS), Azure Container Apps, Terraform, Retail/forecasting/recommendation use cases
Technologies
Azure, Databricks, Python, PySpark, SQL, MLflow, Git, Azure DevOps, GitHub Actions, Docker, Kubernetes, Terraform
Responsibilities
Deploy and operationalize machine learning models; Build and maintain ML and data pipelines; Develop and manage Databricks Workflows, Jobs, MLflow, and model deployment processes; Implement CI/CD pipelines and Git-based development practices; Build monitoring for model performance, data quality, workflow failures, and operational health; Manage model lifecycle activities including versioning, deployment, testing, and continuous improvement; Collaborate with platform, cloud, DevOps, security, and operational teams; Create deployment documentation, runbooks, and support processes.