MLOps Engineer

Siro Clinpharm; United States; Remote; USD 130,000 - 180,000; Full time

Job summary

SIRO Clinpharm Pvt. Ltd. is seeking an experienced MLOps Engineer for a remote role in the United States. You will own ML lifecycle management, deployment, monitoring, and governance across cloud environments (AWS/Azure/GCP).

Ideal candidates bring hands-on expertise in Kubernetes, CI/CD, and scalable ML pipelines, collaborating closely with data scientists and software engineers to operationalize ML solutions in production.

Qualifications

• Hands-on experience operationalizing ML models in production.
• Designing scalable ML pipelines and governance frameworks.
• Proficiency with cloud-native MLOps and infrastructure as code.

Responsibilities

• Design end-to-end MLOps workflows for training, deployment, monitoring, retraining, and governance.
• Build scalable ML pipelines and automate model lifecycle processes.
• Deploy and manage models on Kubernetes-based platforms.
• Implement CI/CD pipelines for ML solutions and track experiment metrics.
• Develop model versioning, registry frameworks, and observability dashboards.
• Monitor performance, detect drift, and automate retraining workflows.
• Collaborate with data scientists and engineers to operationalize ML solutions.
• Implement IaC and cloud-native MLOps best practices.

Skills

MLOps

Kubernetes

CI/CD

ML Platform

Model Deployment

Monitoring

Cloud Infra

Job description

MLOps Engineer | 7+ Years | Remote

JD

We are seeking an experienced MLOps Engineer with strong expertise in ML lifecycle management, Kubernetes, CI/CD, ML platform engineering, model deployment, monitoring, and cloud infrastructure. The ideal candidate will have hands‑on experience operationalizing machine learning models, building scalable ML pipelines, and implementing production‑grade MLOps solutions across AWS, Azure, or GCP environments.

Key Responsibilities

• Design and implement end-to-end MLOps workflows covering training, deployment, monitoring, retraining, and governance.
• Build scalable ML pipelines and automate model lifecycle processes.
• Deploy and manage ML models on Kubernetes-based platforms.
• Implement CI/CD pipelines for machine learning solutions.
• Develop experiment tracking, model versioning, and model registry frameworks.
• Monitor model performance, detect drift, and automate retraining workflows.
• Build dashboards, alerting mechanisms, and observability solutions.
• Collaborate with Data Scientists and Engineers to operationalize ML solutions.
• Implement Infrastructure as Code and cloud-native MLOps best practices.