Senior DevOps Engineer - Docker/Kubernetes (6-15 yrs)

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 team!

Responsibilities :

- 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 downtime.

Requirements :-

- 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.