About EdgeUno
EdgeUno is a US-based technology infrastructure company headquartered in Miami, with a strong operational presence across Latin America, including Colombia, Brazil, Mexico, Argentina, Peru, and Ecuador. We enable digital businesses to scale with high performance and reliability by providing connectivity, IP Transit, private networks, data centers, bare metal, and cloud solutions to ISPs, hyperscalers, content providers, and global technology companies.
Through our own infrastructure platform and strategic interconnection with major global hubs, we deliver low latency, security, and operational resilience across the Americas and beyond. Our Cloud Engineering team is actively expanding EdgeUno’s cloud product portfolio across our LATAM infrastructure footprint, with a strong focus on Kubernetes-based distributed cloud platforms, automation, observability, and AI-driven operational efficiency.
Role Overview
We are looking for an SRE & AI Automation Engineer to join our Cloud Engineering team, helping build the reliability, observability, and automation foundations that support EdgeUno’s growing cloud infrastructure across Latin America.
This role combines two strategic areas: Site Reliability Engineering and AI-powered operational automation. The ideal candidate should be comfortable operating production infrastructure environments while also building intelligent automation workflows, internal AI agents, and operational tooling that improve efficiency, reduce toil, and accelerate infrastructure delivery.
You will work closely with Cloud Engineering leadership and cross-functional teams to help scale modern infrastructure platforms, observability systems, Kubernetes operations, and AI-assisted workflows across EdgeUno’s distributed environment.
Core Responsibilities
Site Reliability Engineering
- Define and implement SLOs, SLIs, and reliability practices across cloud services
- Build and maintain observability environments using Prometheus, Grafana, Alertmanager, Loki, and related tooling
- Reduce operational toil through automation and infrastructure engineering initiatives
- Support incident management processes, post-mortems, runbooks, and operational workflows
- Collaborate on Kubernetes operations, cluster lifecycle management, and infrastructure scalability
- Implement GitOps workflows using tools such as ArgoCD, Flux, and Infrastructure-as-Code frameworks
AI & Automation Engineering
- Design and develop AI-powered operational tools and internal assistants
- Build automation workflows integrating cloud APIs, ticketing systems, Slack, dashboards, and operational platforms
- Integrate LLMs and AI services into internal workflows using APIs and RAG architectures
- Develop AI-driven reporting, incident summarization, and operational intelligence solutions
- Evaluate and prototype agentic AI frameworks and automation platforms
Platform & Infrastructure Automation
- Develop Infrastructure-as-Code environments using Terraform, Ansible, and related technologies
- Build CI/CD pipelines and infrastructure validation workflows
- Automate provisioning, upgrades, monitoring, and infrastructure operations across distributed environments
- Improve deployment reliability and operational visibility across cloud services
Cross-Team Collaboration
- Help establish SRE best practices across engineering teams
- Collaborate with infrastructure, support, operations, and leadership teams to identify automation opportunities
- Maintain clear technical documentation for systems, workflows, and operational processes
- Support tooling evaluation and technical decision-making related to cloud infrastructure and AI operations
Requirements
- 5+ years of experience in SRE, DevOps, Platform Engineering, or related infrastructure roles
- Strong experience with observability and monitoring stacks such as Prometheus, Grafana, Alertmanager, Loki, or equivalent
- Hands-on experience building or integrating AI/LLM-powered applications, tools, or workflows
- Strong proficiency in Python and/or TypeScript
- Experience operating Kubernetes environments in production
- Experience with Infrastructure-as-Code and automation tooling such as Terraform, Ansible, ArgoCD, or similar
- Strong understanding of SLOs, SLIs, reliability engineering, and operational best practices
Strong Differentiators
Experience with workflow automation platforms such as n8n- Experience building RAG pipelines and working with vector databases such as Qdrant, Pinecone, or Weaviate
- Familiarity with AI agent frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar
- Experience with K3s, K0s, Kamaji, Cluster API, or multi-cluster Kubernetes environments
- Experience with Proxmox, Ceph, MinIO, Cilium, eBPF, or distributed infrastructure environments
- Background working for cloud providers, infrastructure companies, or telecommunications environments
- Experience with networking fundamentals such as BGP and connectivity environments
- GitHub or portfolio demonstrating infrastructure automation, AI tooling, or SRE-related projects
What We Offer
Opportunity to work on strategic cloud and AI infrastructure initiatives across Latin America- Direct exposure to modern cloud-native, Kubernetes, and AI-driven operational environments
- Close collaboration with Cloud Engineering leadership and product strategy initiatives
- Multinational and multicultural team environment across LATAM
Portfolio Requirement
Applicants must include a portfolio, GitHub, GitLab, or other practical examples demonstrating relevant technical work.
We are looking for evidence of real systems, automation workflows, AI tooling, infrastructure projects, operational artifacts, or engineering initiatives built and maintained in practice.
