DevOps Engineer

www.youtube.com/shorts/sVlLYFqXujk

Tech Stack:

— Cloud & IaC: AWS (primary), Terraform;
— Containers & Orchestration: Kubernetes (on-prem and cloud) • EKS • Docker • Helm;
— GitOps & CI/CD: Argo CD • Jenkins • GitLab CI;
— Observability: Prometheus • Grafana • ELK (Elasticsearch, Logstash, Kibana);
— Databases & Queues: PostgreSQL • Redis • MongoDB • RabbitMQ.

Requirements:
— Proficiency in leveraging AI-powered tools to automate and accelerate DevOps workflows.
— 3 + years of hands-on DevOps experience in production environments.
— Extensive experience with AWS (GCP or Azure is a plus).
— Strong expertise in Terraform and Kubernetes (both on-premises and in the cloud).
— Solid experience with Helm and Docker.
— CI/CD pipeline design and maintenance using Jenkins and/or GitLab CI; excellent Git skills.
— 5 + years working with Linux systems.
— Proven track record building highly available, scalable, and reliable infrastructure.
— Good understanding of networking fundamentals.
— Proficiency in scripting (Bash, Python, or Go).
— Experience administering PostgreSQL, MongoDB, and Redis.
— Hands-on with monitoring/alerting stacks such as Prometheus, Grafana, Zabbix, or CloudWatch.
— Configuration-management expertise with Ansible.
— Self-sufficient, highly hands-on engineer and an excellent team player with strong communication skills.
— Comfortable working in a fully remote, distributed team.

Responsibilities:
— Support and maintain several existing projects and their infrastructure.
— Drive DevOps activities for new projects from inception to production.
— Investigate and resolve infrastructure-related issues and incidents.
— Design and provision infrastructure using Terraform (EKS/Kubernetes, Docker Compose, AKS, etc.).
— Containerize applications and prepare robust CI/CD pipelines for automated deployment.
— Troubleshoot performance, reliability, and security issues across the stack.
— Migrate legacy infrastructure to Terraform-based infrastructure-as-code.
— Continuously improve automation, observability, and reliability—leveraging AI tools wherever they add value.