Role Overview
We are looking for a hands-on DevOps / Cloud Infrastructure Engineer to build, automate, and operate reliable cloud platforms across multiple environments. The role focuses on Kubernetes and AWS infrastructure, CI/CD and GitOps, Infrastructure as Code, observability, security, and production operations. You will work closely with engineering teams to improve deployment speed, platform reliability, scalability, security, and operational efficiency.
Main Responsibilities
- Design, provision, maintain, and improve cloud infrastructure across development, staging, and production environments.
- Operate Kubernetes platforms, particularly AWS EKS, and manage application delivery using Helm, ArgoCD, and GitOps workflows.
- Build and maintain CI/CD pipelines and automation on GitHub and GitLab to support repeatable, secure, and reliable deployments.
- Develop and maintain Infrastructure as Code using Terraform; use configuration-management and scripting tools to reduce manual operations.
- Implement and improve monitoring, logging, alerting, and observability using Prometheus, Grafana, ELK / OpenSearch, and related tools.
- Troubleshoot infrastructure, networking, Linux, Kubernetes, application delivery, and database-related incidents in production environments.
- Apply security best practices across cloud, network, Kubernetes, identity, secrets, and access-control layers.
- Support relational and NoSQL data platforms, including MySQL, PostgreSQL, and MongoDB, from an infrastructure and operational perspective.
- Contribute to platform reliability, incident response, disaster recovery, capacity planning, and AWS cost optimization initiatives.
- Create and maintain architecture diagrams, runbooks, operational documentation, and technical presentations that clearly communicate system design and decisions.
Must-have Qualifications
- Experience: 2-3 years of hands-on experience as a DevOps, Cloud, Infrastructure, or Platform Engineer.
- Containers & Kubernetes: Solid working knowledge of Docker and Kubernetes, particularly AWS EKS, with hands-on experience using Helm and ArgoCD.
- Cloud Platforms: Practical experience with cloud and edge platforms such as AWS, Google Cloud, and Cloudflare.
- CI/CD & GitOps: Experience building CI/CD pipelines, automation, and developer tooling on GitHub and GitLab, with practical exposure to GitOps practices using ArgoCD and Argo Workflows.
- Infrastructure as Code: Hands-on experience with Terraform. Experience with Ansible is a plus.
- Observability: Working knowledge of monitoring, logging, and alerting tools such as ELK / OpenSearch, Prometheus, and Grafana.
- Linux & Automation: Strong Linux administration skills on Ubuntu and CentOS, with shell scripting and automation using Bash and Python.
- Networking, Security & Databases: Good understanding of networking and security fundamentals, plus operational knowledge of SQL databases (MySQL, PostgreSQL) and NoSQL databases (MongoDB).
- Communication: Ability to explain infrastructure and system architecture clearly, including presenting technical designs in visual form using architecture diagrams or equivalent documentation.
Nice-to-have Qualifications
- Security & Compliance: Knowledge of information security practices and standards such as PCI DSS and ISO 27000 / ISO 27001.
- Tracing, APM & SRE: Experience with distributed tracing and APM tools such as SigNoz or Jaeger, together with SRE practices including SLO/SLA management, incident response, and disaster recovery.
- Secrets & Identity: Hands-on experience with secrets management using Vault and identity / SSO platforms such as Keycloak.
- Cloud Cost Optimization: Experience analyzing and optimizing AWS infrastructure costs, including resource sizing, utilization, and architecture-level cost improvements.
- AI Infrastructure: Exposure to AI infrastructure and modern AI application stacks, including vector databases such as Qdrant, LLM serving, RAG, and Knowledge Base architectures.
- AI-assisted Engineering: Effective use of AI assistants such as Claude, ChatGPT, or similar tools for automation, debugging, operational analysis, and documentation, with awareness of responsible AI use and data privacy.
- Production Scale: Familiarity with supporting large-scale, multi-environment production systems and working with reliability, performance, and operational constraints at scale.
What Success Looks Like
You are comfortable working hands-on with cloud infrastructure and production systems, can automate repetitive operational work, and can troubleshoot issues across multiple layers of the stack. You balance delivery speed with reliability and security, communicate technical decisions clearly, and continuously look for ways to simplify operations and improve the engineering platform.