Optimize Machine Learning Pipelines
17 hours ago
As a Machine Learning Operations (MLOps) Engineer, you will play a critical role in designing and managing the infrastructure and pipelines that support the full lifecycle of machine learning applications. This includes continuous integration, deployment, and monitoring of ML models using tools like GitOps, Jenkins, and GitHub Actions.
We are seeking an experienced professional with hands-on expertise in Docker, Kubernetes, and cloud-based ML platforms such as SageMaker and Vertex AI. You will work closely with data scientists, software engineers, and product teams to integrate ML solutions into production environments.
This is an exciting opportunity to join our team and contribute to the development of scalable, secure, and efficient ML operations for high-impact projects. As an MLOps Engineer, you will have the chance to:
- Design and implement MLOps pipelines for continuous integration, deployment, and monitoring of ML models
- Manage containerization and orchestration using Docker, Kubernetes, and cloud-based ML platforms
- Build and maintain ML orchestration workflows using Kubeflow, Metaflow, MLFlow, and TFX
- Develop infrastructure using Infrastructure-as-Code (IaC) tools like Terraform and AWS CloudFormation
- Monitor model performance and pipeline health using Prometheus, Grafana, and ELK stack
Additionally, you will be responsible for implementing observable pipelines with metrics tracking, concept drift, and model drift detection, automating model retraining, rollback mechanisms, and online or offline validation processes, and optimizing large-scale MLOps pipelines to meet architectural, operational, and organizational demands.
To succeed in this role, you will require a Bachelor's or Master's degree in Computer Science, Engineering, or a related field, and 8+ years of experience in AIML engineering and research with MLOps. Proven experience in MLOps, DevOps, or platform engineering roles is essential, along with hands-on experience with Docker, Kubernetes, and cloud ML platforms, solid understanding of CI/CD practices for ML workflows, familiarity with monitoring tools and performance tracking in production environments, knowledge of security and compliance standards in ML systems, and proficiency in ML orchestration tools and IaC frameworks.
We offer a comprehensive benefits package, including medical, dental, and vision insurance, retirement savings plan, paid time off, and opportunities for career growth and professional development. If you are passionate about machine learning and operations, and possess the skills and experience required for this role, please submit your application today.
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