Role Overview

GLS is hiring a Full Stack Developer to build and maintain the internal tools and operational platforms that run our network business: monitoring dashboards, automation utilities, workflow tooling, and systems that integrate with live infrastructure across a multi datacenter environment. This role is for someone who can ship quickly without sacrificing correctness — using modern AI developer tools (e.g., Claude Opus) and agent frameworks (e.g., OpenClaw) to reduce cycle time, while still delivering reviewed, tested, production quality code. Important: We strongly support AI-assisted development. We do not want vibe coding (blindly accepting AI-generated diffs). We expect engineers to own outcomes through design, review, testing, security hygiene, and maintainability.

What You'll Build

Internal operational tooling that engineers and NOC/SOC teams rely on daily. A custom monitoring/metrics platform spanning time-series data, dashboards, and alerting workflows. Integrations with live network/infrastructure APIs (devices, services, systems). Automation that reduces toil and speeds execution with auditability and guardrails.

Key Responsibilities

Backend Engineering (Go + Python) • Design and implement backend services and REST APIs in Go and Python. • Build durable internal services used for operational workflows and monitoring workloads. • Create clean abstractions around infrastructure and device APIs.

Frontend Engineering (Angular) • Build modern internal UIs in Angular for dashboards, tooling, and operational visibility. • Deliver practical UX: fast, readable, and optimized for operators under pressure.

Data & Database Engineering (PostgreSQL + ClickHouse) • Design schemas and write performant queries for PostgreSQL. • Work with ClickHouse (or similar columnar/time-series systems) for large-scale metrics. • Optimize query patterns and data lifecycle for operational analytics and dashboards. Infrastructure Integration

• Build software that interfaces with network/infrastructure APIs and operational platforms. • Work closely with systems/network engineers to translate real-world requirements into reliable software. Automation & GitOps Workflows

• Build automation utilities using Python and Bash. • Support Git-based workflows, CI/CD, and GitOps conventions where appropriate. AI-Assisted Engineering Expectations We expect you to use AI tools to move faster — and we measure success by quality shipped, not lines generated. You will: Use AI developer tools (e.g., Claude Opus) for acceleration: scaffolding, refactors, test generation, troubleshooting, and documentation — while keeping engineering ownership and rigor. Apply agent frameworks (preferably OpenClaw) to automate repeatable workflows (e.g., repo tasks, environment actions, operational runbooks), including safe execution models (tool policies / sandboxing / hooks or auditing patterns). Build and maintain guardrails: code review discipline, test coverage, static analysis, and secure-by-default patterns. We do NOT want: Vibe coding: accepting AI-generated code without understanding/reviewing/testing it.

Languages: Strong proficiency in Go, Python, and JavaScript/TypeScript.

Frontend: Hands-on Angular experience building internal dashboards/tools.

Databases: Strong PostgreSQL experience; exposure to ClickHouse (or similar) preferred; familiarity with caching (e.g., Redis)

Linux & Delivery: Comfortable in Linux environments; experience with Docker; basic Kubernetes/container familiarity; Git-based workflows.

API Engineering: Proven experience building/consuming REST APIs and integrating with external/internal systems.

Tooling: Git, Docker, Ansible (or similar configuration management).

Communication: Can work independently on loosely-defined problems and communicate clearly with infrastructure-focused teams.

Nice to Have (Strong Signals)

The following skills and experience are not required but demonstrate valuable depth and would accelerate your impact in this role: • Practical OpenClaw experience: multi-agent setups, sandbox/tool allow/deny policies, hooks, automation patterns • GitOps tooling: ArgoCD, Fleet, Helm, operators • Networking fundamentals: IP, VLANs, routing, firewall concepts • Observability experience: metrics pipelines, time-series systems, log pipelines (e.g., Elasticsearch)

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