Senior DevOps Engineer
4 weeks ago
Job Description :
We are looking for a skilled DevOps Engineer with a strong background in AI/ML and extensive experience with AWS, Kubernetes, and container-based architectures. If you have a passion for building robust infrastructure and supporting advanced machine learning applications, we'd love to have you on our :
- Infrastructure Management : Design, deploy, and manage scalable infrastructure on AWS, including services like EBS (Elastic Block Store) and GPU servers for AI/ML workloads.
- Kubernetes & EKS : Set up and maintain Kubernetes clusters using Amazon EKS, ensuring high availability and optimal performance.
- CI/CD Pipelines : Develop and manage continuous integration and continuous deployment pipelines to streamline application updates and deployments.
- Microservices Architecture : Support and maintain a microservices-based architecture, ensuring seamless integration and communication between services.
- Containerization : Utilize Docker and other container technologies to package and deploy applications consistently across environments.
- Monitoring & Logging : Implement monitoring and logging solutions to track system performance, identify issues, and ensure reliability.
- Security : Ensure the security of the infrastructure by implementing best practices and managing access controls.
- Collaboration : Work closely with AI/ML engineers, software developers, and other team members to support their infrastructure and deployment needs.
- Automation : Automate repetitive tasks to improve efficiency and reduce the potential for human error.
- Troubleshooting : Diagnose and resolve infrastructure and deployment issues promptly to minimize :-
- Experience : 5+ years of experience in DevOps or a similar role, preferably within AI/ML or technology-focused companies.
- AWS Expertise : Deep understanding of AWS services, including EC2, EBS, S3, RDS, and especially EKS (Elastic Kubernetes Service).
- Kubernetes : Hands-on experience with Kubernetes for container orchestration and management.
- Containerization : Proficiency with Docker and container-based deployment strategies.
- Microservices: Experience working with microservices architecture and managing inter-service communication.
- CI/CD Tools : Familiarity with CI/CD tools such as Jenkins, GitLab CI, CircleCI, or similar.
- Scripting : Strong scripting skills in languages like Python, Bash, or similar for automation tasks.
- GPU Servers : Experience managing and optimizing GPU servers for AI/ML workloads is highly desirable.
- Monitoring Tools : Knowledge of monitoring and logging tools like Prometheus, Grafana, ELK Stack, or similar.
- Problem-Solving : Excellent problem-solving skills and the ability to troubleshoot complex infrastructure issues.
- Team Player : Strong communication skills and the ability to work effectively within a collaborative team environment.
Preferred Qualifications :
- Education : Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- Certifications : AWS Certified DevOps Engineer, Kubernetes Certified Administrator, or similar certifications.
- Automation Tools : Experience with Infrastructure as Code (IaC) tools like Terraform or CloudFormation.
- Security : Knowledge of security best practices and experience implementing security measures in cloud environments.
- AI/ML Exposure : Previous experience working in AI/ML projects or with machine learning frameworks. (ref:hirist.tech)
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