DevOps Engineer

AI-assisted engineering is a core aspect of this role. You are expected to actively use AI tools, automate repetitive tasks, and demonstrate measurable improvements in workflows.

Responsibilities

- Build and maintain AWS infrastructure, including VPC, EKS, EC2, load balancing, storage, and IAM.
- Manage infrastructure as code using Terraform, ensuring environment provisioning and changes are reproducible and traceable.
- Operate Kubernetes clusters and maintain Helm charts, resource configuration, autoscaling, and configuration management.
- Develop and improve CI/CD pipelines and GitOps workflows using tools such as Argo CD.
- Implement monitoring, logging, tracing, and actionable alerts to enhance incident detection and diagnosis.
- Participate in production incident response, root cause analysis, and corrective actions. Maintain runbooks and validate backup and disaster recovery procedures.
- Strengthen infrastructure security through least-privilege access, network isolation, secrets management, vulnerability scanning, and patching.
- Collaborate with developers to resolve deployment, capacity, performance, and environment issues.
- Use AI coding assistants and agent tools to accelerate infrastructure development, pipeline optimization, troubleshooting, and documentation.
- Combine AI with scripts and internal tools to automate recurring tasks and build self-service capabilities.
- Validate AI-generated code, configurations, and operational recommendations before use, with appropriate testing and rollback procedures.
- Measure improvements through shorter delivery cycles, fewer manual steps, and faster incident diagnosis, and share effective practices across the team.

Requirements

- At least 3 years of experience in DevOps, SRE, or cloud infrastructure, including hands-on production operations.
- Strong knowledge of AWS services and the ability to troubleshoot networking, compute, storage, and access control issues.
- Solid understanding of Linux, TCP/IP, DNS, TLS, and load balancing.
- Hands-on experience with Docker, Kubernetes, and Helm, including scheduling, resource management, health checks, storage, and cluster upgrades.
- Practical experience with Terraform, including reusable modules, state management, and safe infrastructure changes.
- Experience building and maintaining CI/CD pipelines, with familiarity with platforms such as GitLab CI or GitHub Actions and an understanding of GitOps.
- Familiarity with Prometheus, Grafana, OpenTelemetry, or equivalent observability tools.
- Ability to build reliable automation using Python, Go, or Shell.
- Regular use of AI coding assistants or agent tools, such as Claude Code, Codex, GitHub Copilot, or equivalents, in engineering workflows.
- Ability to provide concrete examples of AI-driven efficiency improvements, explaining the original workflow, implementation, validation, and results.
- Sound judgment when reviewing AI output and handling production access, sensitive information, and operational changes.
- Strong communication, documentation, and problem-solving skills, with ownership of issues through resolution.

Nice to Have

- Experience with financial trading, real-time data platforms, or other high-availability systems.
- Experience with Argo CD and multi-cluster, multi-account, or multi-region infrastructure.
- Production experience with Kafka, Redis/Valkey, PostgreSQL, or ClickHouse.
- Familiarity with SLI/SLO practices, capacity planning, load testing, and disaster recovery.
- Experience with cloud security incident response, access governance, or compliance audits.
- Experience deploying and diagnosing .NET applications.
- Experience building AI-assisted internal tools or automated engineering workflows.