Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Automate data workflows and manage machine learning models using cutting-edge tools.
- Company: Join Cloud Bridge, a leader in innovative cloud solutions.
- Benefits: Enjoy flexible work options and access to the latest tech.
- Why this job: Be part of a dynamic team shaping the future of machine learning.
- Qualifications: Proficiency in Python, Bash, and experience with cloud infrastructure required.
- Other info: Ideal for tech-savvy individuals eager to make an impact.
The predicted salary is between 36000 - 60000 £ per year.
Skills and Experience
- Data Workflows: Experienced in automating data workflows with tools such as MLflow, DVC, and SageMaker Model Registry to ensure data availability and traceability.
- Model Management: Proficient in Python, Bash, and scripting for automating model management, training, and deployment processes.
- Cloud Infrastructure: Knowledgeable in cloud infrastructure security with experience in EC2, CloudFormation, and DynamoDB for deploying and managing machine learning models.
- Containerization: Skilled in containerization using Docker and Kubernetes to automate ML pipelines.
- CI/CD Tools: Experienced in automating ML pipelines with CI/CD tools like CodePipeline, Jenkins, and GitLab CI.
- Model Versioning: Experience with model versioning tools.
#J-18808-Ljbffr
Machine Learning Ops Engineer in Marlow - Cloud Bridge employer: WorksHub
Contact Detail:
WorksHub Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Ops Engineer in Marlow - Cloud Bridge
✨Tip Number 1
Familiarize yourself with the specific tools mentioned in the job description, like MLflow and DVC. Having hands-on experience or projects showcasing your skills with these tools can set you apart from other candidates.
✨Tip Number 2
Highlight any previous experience you have with cloud infrastructure, especially with AWS services like EC2 and DynamoDB. Be ready to discuss how you've used these technologies in past projects during the interview.
✨Tip Number 3
Showcase your knowledge of containerization by discussing any projects where you've used Docker and Kubernetes. If you can demonstrate a clear understanding of how these tools improve ML workflows, it will strengthen your application.
✨Tip Number 4
Prepare to talk about your experience with CI/CD tools like Jenkins and GitLab CI. Being able to explain how you've automated ML pipelines in the past will demonstrate your practical skills and readiness for the role.
We think you need these skills to ace Machine Learning Ops Engineer in Marlow - Cloud Bridge
Some tips for your application 🫡
Highlight Relevant Experience: Make sure to emphasize your experience with automating data workflows and model management. Mention specific tools like MLflow, DVC, and SageMaker Model Registry that you have used.
Showcase Technical Skills: Clearly outline your proficiency in Python, Bash, and any other scripting languages. Include examples of how you've automated model management, training, and deployment processes.
Detail Cloud Infrastructure Knowledge: Discuss your experience with cloud infrastructure, particularly with EC2, CloudFormation, and DynamoDB. Highlight any projects where you deployed and managed machine learning models in the cloud.
Emphasize CI/CD Experience: Mention your familiarity with CI/CD tools like CodePipeline, Jenkins, and GitLab CI. Provide examples of how you've used these tools to automate ML pipelines and ensure smooth deployments.
How to prepare for a job interview at WorksHub
✨Showcase Your Automation Skills
Be prepared to discuss your experience with automating data workflows using tools like MLflow, DVC, and SageMaker Model Registry. Highlight specific projects where you ensured data availability and traceability.
✨Demonstrate Proficiency in Scripting
Since model management is key for this role, make sure to showcase your proficiency in Python and Bash. Prepare examples of how you've automated model training and deployment processes in previous roles.
✨Discuss Cloud Infrastructure Knowledge
Familiarize yourself with cloud infrastructure security, especially EC2, CloudFormation, and DynamoDB. Be ready to explain how you've used these services to deploy and manage machine learning models effectively.
✨Highlight Containerization Experience
Containerization is crucial for automating ML pipelines. Be prepared to talk about your experience with Docker and Kubernetes, including any challenges you faced and how you overcame them.
