We are looking for an experienced DevOps Engineer with expertise in cloud platforms and Kubernetes to join our team. The ideal candidate will have hands-on experience with AWS, Azure, or GCP, and a strong understanding of Kubernetes for managing containerized workloads. In addition, proficiency in SǪL/NoSǪL databases and monitoring tools such as Prometheus and Grafana will be crucial. Experience in basic Python scripting and a solid understanding of machine learning operations (ML Ops) will be a plus.

Key Responsibilities:

1. Cloud Infrastructure Management:

   • Manage and optimize cloud infrastructure using AWS, Azure, or GCP.
   • Oversee cloud services such as EC2, S3, RDS, and Kubernetes (EKS/GKE/AKS).
   • Ensure high availability and fault tolerance of cloud-hosted environments.

2.      Kubernetes Administration:

• Administer and manage Kubernetes clusters, including statefulsets, deployments, PVCs, and configmaps.
• Monitor and manage Kubernetes workloads and resources.
• Optimize Kubernetes configurations for scalability, performance, and cost-efficiency.

3.      Database Management:

• Manage and administer SǪL (Postgres) and NoSǪL (MongoDB) databases.
• Experience deploying databases in containerized environments (Kubernetes).
• Ensure database reliability, backup strategies, and optimize database performance.

4.      Monitoring and Alerting:

• Set up and maintain monitoring and alerting systems using tools like Prometheus, Grafana, and CloudWatch.
• Ensure system reliability and quick incident resolution through effective alerting and monitoring setups.

5.      CI/CD Pipeline Management:

• Design, implement, and maintain CI/CD pipelines for automated build, test, and deployment processes.
• Collaborate with software engineering teams to integrate the pipelines with development workflows.

6.      Scripting and Automation:

• Develop automation scripts in Bash and Python for routine tasks, monitoring, and infrastructure management.
• Enhance deployment processes and infrastructure automation.

7.      Collaboration and Communication:

• Work closely with cross-functional teams, including software engineers, cloud architects, and data scientists.
• Communicate effectively to ensure smooth collaboration across teams and with external partners.

8.      Machine Learning Operations (ML Ops):

• Familiarity with ML Ops practices is a plus for collaborating with data science teams.
• Experience with machine learning Python libraries (e.g., TensorFlow, Scikit-learn) is an added advantage.

Required Skills:

1. Cloud Services:
   • Hands-on experience with AWS, Azure, or GCP for managing cloud- based infrastructure.
   • Familiar with cloud-native tools and services such as EC2, S3, IAM, RDS, and Kubernetes services (EKS, GKE, AKS).

2.      Kubernetes Administration:

• Strong experience with Kubernetes administration, especially managing statefulsets, deployments, PVCs, configmaps, and other Kubernetes resources.
• Ability to troubleshoot and optimize Kubernetes-based workloads.

3.      Database Management:

• Solid experience with SǪL databases (e.g., PostgreSǪL) and NoSǪL databases (e.g., MongoDB).
• Experience deploying and managing databases on Kubernetes.

4.      Scripting and Automation:

• Proficient in scripting languages such as Bash and Python for automation of infrastructure management tasks.
• Experience with Git-based workflows for source control and CI/CD.

5.      Monitoring and Alerting:

• Experience with monitoring tools such as Prometheus, Grafana, and CloudWatch to track cloud resources and Kubernetes workloads.
• Knowledge of alerting best practices to ensure system reliability and availability.

6.      Soft Skills:

• Strong communication skills in English, with the ability to collaborate with diverse teams effectively.
• Excellent problem-solving skills and attention to detail.

Ǫualifications:

• Education: Bachelor’s degree in Computer Science, Information Technology, or a related field.
• Certifications: Certifications in cloud platforms (AWS, Azure, GCP), Kubernetes, or DevOps are a plus.

Preferred Ǫualifications:

• ML/AI Experience: Familiarity with ML Ops concepts and machine learning Python libraries like TensorFlow, Keras, or Scikit-learn will be advantageous.
• Containerization Tools: Knowledge of containerization tools (Docker, Helm) and Kubernetes management tools (kubectl, Helm).

Why Join Us?

• Competitive Compensation: We offer a highly competitive salary and performance-based incentives.
• Innovative Culture: Work in a fast-paced, dynamic environment with a focus on cutting-edge technologies.
• Shaping the Future: Contribute to innovative projects that impact industries through scalable and reliable DevOps practices.
• Professional Growth: Opportunities for continuous learning and career development in the cloud and DevOps field.
• Work-Life Balance: Flexible working hours and a culture that values both personal and professional life.