MLOps Engineer: Build & Scale Production ML Pipelines

Remote DXB; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Fusemachines is seeking an experienced MLOps Engineer in the United States to design and operate end-to-end ML pipelines, productionize models, and manage scalable ML platforms for reliable deployments. You will collaborate with data scientists, software engineers, and product teams to ensure governance and efficiency across the ML lifecycle.

Role emphasizes automated testing, monitoring, and cost-efficient infrastructure using Azure Databricks, MLflow, Delta Lake, and Kubernetes, while

Qualifications

• 3+ years of hands-on experience building and operating production MLOps pipelines.
• 3+ years of production ML deployment and automation with CI/CD, PySpark, Docker/Kubernetes, and Azure ML or Databricks.
• Experience focused on MLOps/platform engineering and productionizing ML solutions.

Responsibilities

• Design, implement and maintain end-to-end MLOps workflows for training, deployment, monitoring, retraining, and retirement.
• Productionize data science assets by turning notebooks and prototypes into modular Python packages and services.
• Build and manage CI/CD pipelines for ML 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.

Skills

MLOps engineering

CI/CD for ML

PySpark

Docker & Kubernetes

Azure ML or Databricks

Tools

Azure Databricks

MLflow

Delta Lake

Unity Catalog

Docker

Kubernetes

Azure Machine Learning

Databricks Model Serving

Job description

Fusemachines is seeking an experienced MLOps Engineer in the United States to design and operate end-to-end ML pipelines, productionize models, and manage scalable ML platforms for reliable deployments. You will collaborate with data scientists, software engineers, and product teams to ensure governance and efficiency across the ML lifecycle.

Role emphasizes automated testing, monitoring, and cost-efficient infrastructure using Azure Databricks, MLflow, Delta Lake, and Kubernetes, while