Senior Specialist
4 days ago
Job Description :
As an MLOps Engineer, you will be responsible for the design, development, integration and deployment of frameworks, tools and pipelines required for the end-to-end machine learning lifecycle in customer projects. You will apply MLOps technologies to operationalize AI solutions at scale, integrated with client's business processes and applications. You will be responsible for providing technical contribution and/or leadership in the creation and delivery of MLOps solutions designed to meet customers' business needs and drive outcomes for the customers.
Responsibilities :
1. Infrastructure and Environment Management :
- Design, implement, and manage scalable and reliable infrastructure for machine learning models and applications.
- Ensure seamless integration of machine learning models into production systems.
2. Continuous Integration/Continuous Deployment (CI/CD) :
- Develop and maintain CI/CD pipelines for automating the deployment of machine learning models.
- Implement version control and release management practices for machine learning assets.
3. Model Deployment and Monitoring :
- Deploy machine learning models to production environments and monitor their performance.
- Implement monitoring solutions to detect and address issues related to model drift, data quality, and system health.
4. Collaboration with Data Scientists and Engineers :
- Collaborate with data scientists to understand model requirements and facilitate the transition of models from research to production.
- Work closely with software engineers to integrate machine learning solutions into existing applications.
5. Security and Compliance :
- Implement security best practices for machine learning systems.
- Ensure compliance with relevant regulations and industry standards.
6. Automation and Scripting :
- Develop automation scripts and tools to streamline the MLOps processes.
- Implement best practices for code and configuration management.
7. Troubleshooting and Incident Response :
- Provide support for troubleshooting issues related to machine learning models in production.
- Develop and implement incident response plans for machine learning systems.
8. Documentation :
- Maintain comprehensive documentation for MLOps processes, workflows, and configurations.
Required Skills :
- Strong proficiency in Linux systems administration.
- Experience with Kubernetes for container orchestration.
- Knowledge of data platforms and experience with data processing tools.
- Familiarity with machine learning platforms, frameworks, and tools.
- Experience with MLOps platforms and tools (e.g., MLflow, Kubeflow, TensorFlow, PyTorch,).
- Understanding of AI infrastructure and deployment.
- Proficient in scripting languages (e.g., Python, Bash).
- Experience with CI/CD tools (e.g., Jenkins, GitLab CI).
- Understanding of cloud platforms (e.g., AWS, Azure, GCP).
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