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Job Description
Location
On-site
Type
Full Time
Overview
We are looking for an experienced DevOps Engineer to design, implement, and optimize cloud based infrastructure for AI-powered applications. The ideal candidate should have expertise in CI/CD, cloud automation, containerization, and security practices in AWS, Azure, or GCP environments.
Key Responsibilities
- Develop and maintain CI/CD pipelines for AI/ML applications
- Automate cloud infrastructure provisioning using Terraform, CloudFormation, or ARM templates
- Manage and optimize containerized workloads using Docker and Kubernetes
- Ensure high availability, scalability, and security of AI-driven cloud applications
- Collaborate with AI engineers, software developers, and cloud architects
- Monitor system performance, troubleshoot issues, and implement best practices for cloud security
- Implement AI-driven automation and observability solutions using tools like Prometheus, Grafana, or ELK stack
- Stay updated with the latest DevOps, AI, and cloud computing trends
Required Skills & Qualifications
- Bachelor's or Master’s degree in Computer Science, Engineering, or a related field
- 3+ years of experience in DevOps, cloud computing, or infrastructure automation
- Proficiency in AWS, Azure, or GCP cloud services
- Hands-on experience with CI/CD tools (Jenkins, GitHub Actions, GitLab CI/CD, and Azure DevOps)
- Strong knowledge of containerization (Docker, Kubernetes, Helm)
- Experience with scripting and automation using Python, Bash, or PowerShell
- Familiarity with monitoring and logging tools (Prometheus, Grafana, Splunk, ELK stack)
- Knowledge of security best practices for DevOps and cloud infrastructure
- Certifications in AWS (AWS Certified DevOps Engineer), Azure (Azure DevOps Engineer), or GCP (Google Professional DevOps Engineer)
- Experience with MLOps and AI model deployment in cloud environments
- Knowledge of infrastructure-as-code (IaC) best practices
- Experience in serverless computing and event-driven architectures
- Experience integrating AI/ML pipelines with cloud-based DevOps workflows
- Knowledge of serverless computing and event-driven architectures
- Cloud certifications such as AWS DevOps Engineer, Azure DevOps Engineer Expert, or Google Professional DevOps Engineer
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