Senior DevOps Engineer
Description
About Us
At SkyLabs AI Inc., we are at the forefront of the artificial intelligence revolution. As a US-headquartered company, we conduct applied research on AI for intelligent reasoning. We specialize in complex neurosymbolic AI to solve intricate problems within software engineering. Our team is composed of world-class researchers and engineers dedicated to building the platforms and intelligent agents that will power the next generation of software. If you are passionate about building truly intelligent systems and want to make a lasting impact, join us.
The Role
We are seeking a highly skilled Senior DevOps Engineer to join our innovative team dedicated to revolutionizing the AI landscape. As a key player in our organization, you will have the opportunity to streamline our development and operational processes, ensuring that our applications run smoothly and efficiently. This role is ideal for individuals who thrive in a fast-paced environment and are passionate about leveraging technology to enhance productivity. You will work closely with our development and IT teams to implement robust deployment pipelines, automate processes, and optimize system performance. Your expertise will play an essential role in improving collaboration across teams, enabling continuous integration and continuous delivery (CI/CD), and enhancing overall application reliability. At Skylabs AI, we believe in fostering a culture of continuous improvement and innovation, and we are eager to welcome a DevOps professional who shares our vision. Your contributions will directly impact our ability to deliver cutting-edge AI solutions to our clients while maintaining high levels of quality and efficiency. If you are ready to take on new challenges and make a significant impact in the world of AI and technology, we encourage you to apply.
Responsibilities
- Design and implement scalable CI/CD pipelines to streamline software delivery processes.
- Monitor and optimize system performance, ensuring high availability and reliability of applications.
- Collaborate with development teams to integrate automated testing and deployment practices into the workflow.
- Manage cloud infrastructure and services, ensuring cost-effective and efficient resource allocation.
- Implement security best practices throughout the DevOps process and infrastructure as code.
- Troubleshoot and resolve complex infrastructure issues to minimize downtime and maintain service quality.
- Provide training and support to development teams on best practices for CI/CD and infrastructure management.
Requirements
- Bachelor's degree in Computer Science, Engineering, or related field.
- Proven experience as a DevOps Engineer or similar role in software development and system administration.
- Strong knowledge of CI/CD tools such as Jenkins, GitLab CI, or CircleCI.
- Experience with cloud platforms such as AWS, Azure, or Google Cloud.
- Familiarity with containerization technologies like Docker and orchestration tools such as Kubernetes.
- Proficiency in scripting languages such as Python, Bash, or Ruby.
- Solid understanding of networking, security, and server management principles.
Benefits
- Competitive salary in USD
- Comprehensive health allowance
- Relocation allowance (if you're moving to Islamabad)
- Monthly team events and offsites
- A beautiful, collaborative office space
- Work alongside world-class AI researchers and engineers
Skills
As published by workable · 8 questions
Basics
First name, Last name, Email, Headline, Phone, Address, Resume, Cover letter
Short answers (2)
- Expected Salary
- Current Salary
Pick from a list (6)
- Are you comfortable working in a Full-Time Onsite role?
- Do you have 5+ years of professional experience in a DevOps, SRE, or Infrastructure Engineering role?
- Have you authored complex, production-grade Bash scripts that include advanced error handling and modular logic?
- Have you ever successfully optimized GitHub Action workflow execution times through advanced techniques (e.g., custom runners, dependency caching, or build matrices)?
- Do you have direct experience managing infrastructure for AI/ML workloads, specifically involving GPU partitioning, CUDA drivers, or deploying "Large Models" (LLMs)?
- Have you managed high-volume observability data (logs/traces) on S3 and implemented strategies to solve the "small file problem" or "API cost explosion"?
