Platform Engineer (AI Automation)
POSITION SUMMARY
We're seeking a Platform Engineer (I3) who is an AI-native builder, someone who doesn't just use AI tools but designs and deploys AI agents to eliminate toil, accelerate developer workflows, and multiply what a lean team can deliver. You will own solutions across Kubernetes (AWS EKS), GitOps (Argo CD), CI/CD (GitHub Actions), and our developer portal (Cortex), while relentlessly automating repetitive operational work through agentic AI. As an I3 engineer, you are a professional contributor who effectively represents the team's perspective and communicates across functions. You will collaborate with DevX, SRE, and Product Engineering peers to turn fragmented workflows into AI-powered golden paths, building agents that handle incident triage, environment provisioning, config generation, and developer self-service so engineers can focus on high-value work instead of tickets, aligned with our Platform Engineer growth matrix.
This position will ideally be hybrid at our Mexico City office as part of our expanding site, though remote work is possible. EarnIn offers excellent employee benefits, including healthcare, internet and cell phone reimbursement, a learning and development stipend, and potential opportunities to travel to our headquarters in Mountain View. Our salary ranges depend on role, level, and location.
WHAT YOU'LL DO
AI-Agent Development: Design, build, and iterate on AI agents that automate platform operations, from service and infra bootstrap, intelligent incident diagnosis, automated runbook execution, to self-healing infrastructure and PR-review bots. Own the agent lifecycle: prompt engineering, tool/function-call orchestration, evaluation, and production monitoring.
Workflow Automation with AI: Identify and eliminate repetitive human-in-the-loop workflows across CI/CD, environment management, access provisioning, and change management.
Developer Self-Service: Evolve our developer control plane (Cortex) into an AI-augmented self-service platform where engineers interact with intelligent assistants, not just dashboards, to scaffold services, debug deployments, query platform health, and resolve issues without filing tickets.
Delivery & GitOps: Implement and maintain GitOps-based continuous delivery with Argo CD, integrating AI into CI pipelines for intelligent test selection, automated config validation, and deployment risk scoring.
Kubernetes Infrastructure: Operate and evolve our Kubernetes platform on AWS EKS, focusing on environment hygiene, security, and cost efficiency, leveraging AI for anomaly detection, capacity recommendations, and automated remediation.
Observability & Operational Excellence: Strengthen reliability by monitoring SLOs/error budgets and utilizing Datadog for metrics, traces, and logs, building AI-driven alerting and root-cause-analysis agents that reduce mean-time-to-resolution.
Service Standardization: Maintain service scaffolds and templates that encode standards for testing and telemetry, using AI to auto-generate and validate configurations against platform baselines.
WHAT WE'RE LOOKING FOR
Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
3+ years in platform, infrastructure, or backend engineering with deep knowledge of Kubernetes (EKS) and cloud-native architectures on AWS.
Demonstrated experience building AI agents or agentic workflows, not just using Copilot or ChatGPT, but designing multi-step AI systems that autonomously perform operational tasks (e.g., LLM-powered runbook agents, intelligent CI/CD bots, self-service assistants with tool-use/function-calling).
A track record of automating repetitive workflows with AI. You should be able to point to specific examples where you replaced manual processes with AI-driven automation that let a team do more with fewer resources.
Experience in GitOps (ArgoCD) and CI (GitHub Actions) for multi-service systems.
Strong coding skills in Go and/or Python, treating infrastructure as software.
Excellent communication skills; able to clearly convey technical problems and advocate for both team and customer needs.
Experience with LLM orchestration frameworks (e.g., LangChain, LlamaIndex, CrewAI, or custom agent architectures) and prompt engineering for production systems.
Mission-Driven: A strong advocate for the customer who takes initiative to fix issues and constantly asks, "Can an AI agent do this instead of a human?"
#LI-Remote #LI-Hybrid