- 401(k) matching
- Flexible spending account
- Flextime
- Health insurance
- Health savings account
- Paid time off
- Relocation assistance
- Tuition reimbursement
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DevOps Engineer - San Diego, United States - Patterned Learning AI
Description
Job DescriptionDevOps Engineer- Remote Job, 1+ Year Experience
Annual Income: $60K - $65K, Onsite
A valid work permit is necessary in the US/Canada
About us: Patterned Learning is a platform that aims to help developers code faster and more efficiently. It offers features such as collaborative coding, real-time multiplayer editing, and the ability to build, test, and deploy directly from the browser. The platform also provides tightly integrated code generation, editing, and output capabilities.
More About the Role & Team
The ideal candidate should have strong technical skills, hands-on experience with DevOps tools and technologies, and a passion for automation and continuous improvement.
What skills and experience do I need to succeed?
• You will need to have proven experience delivering commercial software applications.
• You will need hands-on experience with DevOps tools and technologies such as Jenkins, Octopus, Docker, Kubernetes, and Git. Experience with cloud-based infrastructure and services, preferably Azure is essential.
About the job requirements:
• DevOps Engineering
• Web development (HTML, CSS, Angular)
• Software Engineering
• Content Management Systems
• Databases and Structured Data
• Flexible and adaptable with the ability to align to changing priorities
Benefits
Patterned Learning can provide intelligent suggestions, automate repetitive tasks, and assist developers in writing code more effectively. This can help reduce coding errors, improve productivity, and accelerate the development process.
The pattern recognition is particularly relevant in the context of coding. Neural networks, especially deep learning models, are commonly employed for pattern detection and classification tasks. These models simulate human decision-making and can identify patterns in data, making them well-suited for tasks like code analysis and generation.