doctorly — DevOps Team Lead
Returning to doctorly to continue evolving the platform built during the 2022–2025 stint below: Kubernetes on AWS, multi-environment Terraform, GitLab CI/CD with security scanning, and ArgoCD-driven deployments.
DevOps Team Lead
Remote-first DevOps leadership. Building reliable cloud platforms — and the teams behind them.
DevOps Team Lead with 15+ years building and leading high-performing remote teams across Europe and the Middle East. I turn infrastructure into a competitive advantage — automating CI/CD pipelines, scaling cloud environments on AWS and Azure, and enabling dev teams to ship faster with confidence. Remote-first by design: I lead fully distributed teams across time zones on a foundation of trust, async communication, and strong engineering culture. I also build the AI tooling my teams run on — a fleet of custom subagents and Claude Code skills, wired into Linear, GitLab, AWS, and Kubernetes over MCP, that automate infrastructure review, GitOps onboarding, and ticket refinement under least-privilege tool scopes, with every AI-assisted change traceable to a ticket. Open to DevOps Lead, Head of DevOps, and DevOps Architect roles — remote or hybrid, B2B or employee.
Replaced slow, manual release processes with automated CI/CD and GitOps so engineering teams could ship multiple times a week instead of once a month.
Cut recurring cloud spend by ~40% through infrastructure optimisation: right-sized compute, consolidated databases, and pruned unused services.
Improved customer-facing reliability with resilient architecture, autoscaling, and proactive operations across production environments.
Grew distributed teams from the ground up across Europe and the Middle East — hiring, structured 1:1s, retros, and mentoring focused on engineer ownership.
Aligned cloud infrastructure design, access controls, and operational processes with the BSI Cloud Computing Compliance Criteria Catalogue (C5) and ISO 27001 information-security standards — meeting the bar for regulated B2B and healthcare-sector deployments.
I run AI agents as production tooling, not as a demo. I build and maintain the agent layer of a live infrastructure repository — custom subagents, skills, and Model Context Protocol integrations that encode team process as executable, version-controlled workflows. The design rule is the one I already apply to infrastructure: least privilege and separation of duties. Agents that review never hold write access, and agents that author are audited by a different agent before anything reaches production.
Built and maintain seven custom subagents for a production infrastructure repo — Terraform, Ansible, ArgoCD, and general DevOps specialists, each scoped to its own tools and source-of-truth docs. Authoring and review are deliberately separate agents: one writes the HCL, another audits it for state safety, blast radius, and least-privilege IAM before any apply.
A review skill fans out five reviewers in parallel — Terraform, Ansible, security, architecture, and conventions-versus-ticket completeness — then merges them into a single report. Verification agents run repo lint plus read-only AWS and kubectl queries, so findings are checked against real cluster state rather than the text of the diff.
Every agent and skill declares an explicit tool allowlist. The review pipeline is granted read verbs only — describe, get, list, logs — and is hard-blocked from write and edit tools entirely, so an agent can inspect production without ever holding a path to change it.
The repo's agent configuration is a source of truth under version control: a maintained operating policy, documented conventions, and a session-start hook that requires every AI session to be tied to a Linear or Jira ticket — so AI-assisted changes stay exactly as traceable as any other change.
An onboarding skill walks a new service through the full CD path: registering ArgoCD applications for dev and production, wiring CI’s image-tag write-back into values, publishing OCI Helm charts and images to ECR, and setting up semantic-release publish workflows.
Documented and standardised the MCP setup — Linear, Notion, the AWS Agent Toolkit, Terraform registry resolution, and diagram rendering — so the whole team runs the same agents against the same shared context instead of each engineer improvising their own prompts.
Returning to doctorly to continue evolving the platform built during the 2022–2025 stint below: Kubernetes on AWS, multi-environment Terraform, GitLab CI/CD with security scanning, and ArgoCD-driven deployments.
Highlights
DevOps Lead & Infrastructure Architect — led the design and delivery of doctorly's complete infrastructure and CI/CD ecosystem from the ground up, while driving the multi-tenancy initiative.
Team leadership
Infrastructure
CI/CD & security
Technical decision-making
As Head of QA (Dec 2018 – Jan 2020)
As DevOps Team Lead (Jul 2018 – Jan 2020)
DevOps System Engineer
IT Manager (parallel role)
Cloud
AWS, Azure
Orchestration & GitOps
Kubernetes (EKS, AKS), Docker, Helm, ArgoCD
Infrastructure as code
Terraform (umbrella modules), Ansible
CI/CD & Security
GitLab CI/CD, GitHub Actions, Jenkins, SAST / dependency / secret / container scanning, C5 (BSI) compliance
Observability
Prometheus, Grafana, CloudWatch
Systems
Linux administration, Git, Nginx, APISIX
AI & agentic engineering
Claude Code skills, subagents & hooks, MCP server integrations (Linear, Notion, AWS Agent Toolkit), agent tool-scoping & least-privilege automation, multi-agent code review, prompt & context engineering, GraphRAG
Leadership
Remote team management, Agile (Scrum/Kanban), Hiring, Mentoring, B2B engagements
Languages
Lebanese (native) · French (native) · English (professional working)
Open to DevOps Lead, Head of DevOps, and DevOps Architect roles — remote, hybrid, B2B or employee.