AI Developer

Job Description

Job Description

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)

What Success Looks Like (First 60–90 Days)

• Ship meaningful improvements to internal tooling and/or monitoring workflows. • Deliver at least one end-to-end feature: UI + API + database + operational integration. • Improve cycle time using AI tools with discipline: tests, review, measurable reliability gains. • Contribute reusable automation patterns that reduce toil for the team (agent or script-based).

Why GLS

You'll build software that directly supports real operational outcomes. Real users, real systems, real impact. High ownership, low bureaucracy, strong technical collaboration with infrastructure experts.

How to Apply

If you're excited about building mission-critical internal tools with modern AI-assisted workflows while maintaining engineering rigor, we'd love to hear from you. Submit your resume and a brief note about your experience with full-stack development, particularly highlighting any AI-accelerated development practices you've used. Include links to relevant projects or code samples if available.