We are looking for a highly skilled MLOps Engineer to build, deploy, automate, and manage Machine Learning solutions in production. The ideal candidate should have hands-on experience in designing scalable ML pipelines, deploying models, automating workflows, monitoring model performance, and implementing CI/CD for machine learning applications. The role requires close collaboration with Data Scientists, Data Engineers, and DevOps teams to ensure reliable and efficient ML model lifecycle management.
Key Responsibilities
Design, develop, and maintain end-to-end MLOps pipelines for training, testing, deployment, and monitoring of machine learning models.
Deploy machine learning models to cloud and on-premise environments using containerization and orchestration technologies.
Build and automate CI/CD pipelines for machine learning applications.
Develop reusable ML workflows for model training, validation, deployment, and versioning.
Implement model monitoring, performance tracking, drift detection, and automated retraining strategies.
Manage ML artifacts, datasets, feature stores, and model registries.
Collaborate with Data Scientists to operationalize machine learning models.
Optimize infrastructure for scalable and cost-effective model deployment.
Troubleshoot production issues and ensure high availability of ML services.
Maintain security, governance, and compliance standards across ML platforms.
Required Skills
5–8 years of IT experience with at least 3+ years of hands-on experience in MLOps.
Strong programming experience in Python.
Hands-on experience with one or more MLOps platforms:
MLflow
Kubeflow
Azure Machine Learning
AWS SageMaker
Google Vertex AI
Experience deploying machine learning models using:
Docker
Kubernetes
Strong knowledge of CI/CD tools:
Jenkins
Azure DevOps
GitHub Actions
GitLab CI/CD
Experience with version control using Git.
Solid understanding of machine learning lifecycle and model management.
Experience with REST APIs for model serving.
Kno
📌 MLops Engineer (India)
🏢 Straive
📍 India