Hoosh Sazeh is looking for a DevOps / Site Reliability Engineer to build, operate, and improve our infrastructure, delivery pipelines, and production services, with a focus on reliability, security, recoverability, and automation.

You will independently handle day-to-day operations and troubleshooting, deliver focused infrastructure improvements, and collaborate closely with software engineers. Practical SRE skills and previous experience setting up working infrastructure with AI agents or agentic workflows are required.

- Operate and troubleshoot Linux servers, VMware/Proxmox environments, and Docker/Docker Compose workloads, including networking, storage, VM lifecycle, snapshots, images, registries, volumes, secrets, and health checks.
- Build and improve GitLab CI/CD pipelines for builds, tests, security checks, artifact publishing, deployment, and rollback of React/Next.js, Go, and Python services.
- Install, upgrade, back up, and maintain self-hosted tools such as GitLab, Jira, and Confluence, including integrations and access management.
- Improve monitoring, logging, and actionable alerting. Track agreed service-level indicators and objectives (SLIs/SLOs), and use error budgets to inform reliability improvements and release decisions.
- Participate in a **scheduled on-call rotation providing 24/7 coverage for critical incidents**. Investigate incidents, restore services, communicate impact, and own resolution or a clear handoff. Contribute to blameless postmortems, root-cause analysis, and preventive actions.
- Apply server hardening, least-privilege access, TLS, secrets protection, and security patching.
- Improve capacity, performance, and resilience. Maintain and test backup/restore, disaster recovery, and rollback procedures against agreed recovery objectives.
- Use AI-assisted and agentic workflows for infrastructure setup and automation. Reduce repetitive operational work and maintain version-controlled configuration, architecture documentation, and operational runbooks.

Core Engineering:
- Strong Linux administration, preferably Ubuntu Server, and practical knowledge of TCP/IP, DNS, HTTP(S), reverse proxies, firewalls, and network troubleshooting.
- Previous hands-on experience setting up a working infrastructure environment using AI agents or agentic workflows, including implementation and validation. Using a chatbot only to generate scripts or recommendations is not sufficient.

Languages & Frameworks:
- Ability to write and maintain automation with Bash or Python.
- Experience deploying and troubleshooting React/Next.js, Go, and Python services, including runtime requirements, configuration, and dependencies.

Architecture & System Design:
- Strong understanding of infrastructure architecture, scalability, optimization, service dependencies, and failure modes.
- Ability to propose maintainable solutions and explain trade-offs between reliability, security, complexity, and delivery speed.

Databases & Data Systems:
- Understanding of persistent storage, data durability, and application-consistent backup and restore; ability to coordinate data recovery with service owners.
infrastructure & Operational Awareness
- Hands-on experience with VMware and/or Proxmox, Docker, Docker Compose, Git, and GitLab CI/CD, including runners and secure deployment workflows.
- Experience maintaining self-hosted engineering tools; equal depth across GitLab, Jira, and Confluence is not required.
- Availability for full-time on-site work and participation in the agreed on-call rotation.

Performance & Reliability:
- Practical experience with observability, incident response, and applying SLIs/SLOs; an understanding of error budgets.
- Ability to investigate availability, latency, errors, and resource saturation, and validate recovery time and recovery point objectives (RTO/RPO) through restore tests.

Security & Production Awareness:
- Practical server hardening, access control, TLS, patching, and secrets management.
- Ability to assess change risk, validate changes before production rollout, and prepare rollback or recovery procedures.

AI-Augmented Engineering Expectations:
- Use AI to support provisioning, configuration, deployment automation, investigation, and documentation. Keep outputs version-controlled, reproducible, and maintainable without relying on the original AI conversation.
- Review generated commands, code, and configuration. Use isolated testing, automated validation, and human approval before production-impacting or destructive actions. Restrict agent permissions, protect sensitive data, maintain an audit trail, and provide rollback or recovery paths.
- Be able to walk through a completed AI-assisted infrastructure setup, explaining the agent’s contribution, your decisions and corrections, and how you verified the outcome.

The engineer remains fully responsible for architectural decisions, correctness, reliability, security, production quality, and maintainability. AI tooling is expected to improve productivity, not replace engineering judgment. No particular AI vendor or agent framework is mandatory.

You improve service reliability, make releases safer, verify that recovery works, reduce noisy alerts and repetitive manual work, and leave behind maintainable automation. You communicate clearly during incidents and follow corrective actions through to completion.