Kubernetes Security Engineer
OpenKyber LLC
1 month ago
Role details
Contract type
Permanent contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
English Experience level
JuniorJob location
Remote
Tech stack
Kubernetes Security
Artificial Intelligence
Amazon Web Services (AWS)
Azure
Bash
Cloud Computing
Continuous Integration
DevOps
Github
Identity and Access Management
Python
Network Security
Reliability Engineering
Prometheus
Systems Integration
TypeScript
Datadog
Google Cloud Platform
Large Language Models
Grafana
Cloudformation
Kubernetes
Performance Monitor
Operational Systems
Machine Learning Operations
Terraform
Docker
Jenkins
Job description
Job Description: Our Fintech client is looking for an experienced DevOps / MLOps Engineer to help build and manage cloud infrastructure, deployment pipelines, and operational systems supporting AI-driven platform initiatives. This is a high-visibility role in a fast-moving environment where candidates are expected to make an immediate impact., * Develop, implement, and maintain CI/CD pipelines using tools such as GitHub Actions, Jenkins, or similar platforms
- Provision and manage cloud infrastructure across AWS, Azure, or Google Cloud Platform using infrastructure-as-code tools like Terraform or CloudFormation
- Support containerized environments and orchestration using Docker and Kubernetes
- Monitor system performance, respond to incidents, and help define and track service reliability metrics (SLOs/SLAs)
- Partner with engineering teams to streamline and improve deployment processes
- Apply security best practices, including identity and access management, secrets handling, and network security policies
- Participate in on-call support as required
Requirements
Do you have experience in System performance monitoring?, * 7+ years of experience in DevOps, Site Reliability Engineering, or platform engineering roles
- Strong hands-on experience with AWS
- Practical experience working with Kubernetes environments (EKS, GKE, or AKS)
- Proficiency with IaC tools such as Terraform
- Solid understanding of CI/CD methodologies and GitOps practices
- Experience with monitoring and observability tools such as Datadog, Grafana, or Prometheus
- Strong scripting ability in languages like Bash, Python, or TypeScript
- 1+ years of experience supporting AI/ML infrastructure, including GPU-based workloads or model deployment
- Familiarity with monorepo build systems such as Nx or similar tools
- Exposure to LLM integrations or AI platform ecosystems
- AWS certifications (e.g., Solutions Architect or DevOps Engineer