Ml Ops Engineer

Description:

Experience required: 6+ Years
Duration: 6 Months to Permanent

Mandatory: Expertise in Azure DevOps with ML Ops integration.

Responsibilities:
The candidate should be well-versed in using tools like Jenkins, GitLab CI, Azure DevOps, or equivalent, and should be able to integrate these tools with machine learning platforms and data repositories.

* Design, implement, and manage CI/CD pipelines tailored for machine learning workflows.
* Ensure that machine learning models are properly versioned and deployed into production, staging, or testing environments automatically.
* Collaborate with data scientists and software engineers to optimize the automation process for training, validating, and deploying machine learning models.
* Continuously monitor the performance and reliability of CI/CD pipelines and make adjustments as necessary.
* Set up environments and fully implement scalable machine learning operations.
TechnicalSkills:
* Minimum of 7 years experience in setting up and maintaining CI/CD pipelines, preferably in a machine learning context.
* Strong understanding of containerization and orchestration tools such as Docker and Kubernetes.
* Familiarity with machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn.
* Proficient in scripting languages such as Python, Bash, or Shell for automation tasks.