Platform Engineer

Strategic Staffing Solutions; Argentina; Hybrid; ARS 2,400,000 - 4,200,000; Full time

Job summary

Strategic Staffing Solutions is sourcing a Platform Engineer for a leading EdTech client in Latin America. The role centers on shaping the development experience, tooling, and agentic architectures that empower a small, AI-native squad to move fast and reliably.

You will partner with the CTO to own how things are built: tools, models, agent skills, testing harnesses, and the CI/CD machinery that powers daily engineering work.

Qualifications

• Fluent English with professional Spanish preferred.
• Proven platform engineering or DevOps experience in fast teams.
• Experience building developer-focused tooling and pipelines.
• Strong infra fundamentals across AWS and IaC.
• Ability to own architecture from tools to CI/CD.

Responsibilities

• Own the development toolchain, build scaffolding and templates.
• Design and own the golden path for development workflows.
• Architect CI/CD pipelines and ephemeral preview environments.
• Create semantic team memory and guardrails for agents.
• Design security, observability, and cost controls for the platform.

Skills

English fluency

Spanish proficiency

Platform engineering

Developer experience mindset

AWS fundamentals

CI/CD as product

LLM tooling

Tools

Claude Code

Cursor

Terraform

AWS (VPC, ECS/EKS, Lambda)

CDK

RAG / Vector DBs

Job description

Our client is a leading EdTech company in Latin America, serving hundreds of thousands of students across the region with live classes, self-paced lessons and mobile apps. They are rebuilding their learning management system from the ground up. It’s the platform where every student’s learning happens, and it’s being built by a small, AI-native engineering squad.

Role Overview

We aren't looking for a traditional DevOps engineer who keeps pipelines green and waits for infrastructure tickets. We are looking for a Platform Engineer: the architect of our development experience, someone who treats the engineering team as their product and builds the agentic machinery that lets a small squad ship like a company several times its size.

Working directly alongside the CTO, you will design and own the development architecture of our clean-sheet, agentic-first build. While our specialized product engineers own what gets built, you own how it gets built: the tools, frameworks, models, agent skills, testing harnesses, and CI/CD machinery that every engineer touches every single day. This role has no direct reports: you lead the platform, not people.

This is a role with a permanent innovation mandate. The AI development landscape reinvents itself every few weeks, and your job is to live on the crest of that wave: evaluating new models, agents, and frameworks as they appear, piloting the ones that matter, and retiring what no longer earns its place. If you want to build the factory that builds the product, this role is for you.

Main Responsibilities

1. Development Architecture & Developer Experience

• Own the Toolchain: Select, configure, and continuously evolve the tools, frameworks, agentic environments (Claude Code, Cursor, custom agents), and model routing strategies that define the team's daily workflow. Every choice is yours to make and yours to defend.
• Design the Golden Path: Build the paved road: project scaffolding, shared configurations, reusable skills, and templates that make the right way to build also the fastest way. Friction anywhere in the dev loop is your bug to fix.
• CI/CD as a Product: Architect agentic CI/CD pipelines, ephemeral preview environments, and infrastructure as code on AWS that take a merged branch to production with zero manual gates and full confidence.

2. The Agentic Engine & Team Memory

• Semantic Team Memory: Design and implement the team's shared memory layer, so that architectural decisions, conventions, resolved bugs, and domain knowledge persist across sessions, agents, and engineers instead of evaporating after every chat.
• Guardrails & Loops: Build the automated generation, self-review, and execution loops that power our agentic-first build engine, wrapped in guardrails that let engineers move at full speed without breaking the platform.
• Skills Arsenal: Create and maintain the library of agent skills, evaluation suites, and multi-agent review prompts that encode how we build, and measure that they actually make the team faster.

3. Quality Automation & Observability

• E2E Testing Frameworks: Own the end-to-end testing architecture that builds quality into the pipeline itself. That includes automated regression suites, self-testing code and production-readiness checks, so engineers own quality and nobody waits for a separate QA phase.
• Instrument the Dev Process: Measure developer experience like a product: lead time, agent performance, token economics, flaky tests, and friction points. Use the data to decide what to improve next, and answer for the results.

4. Constant Innovation & Enablement

• Live on the Crest of the Wave: Continuously scan the frontier of AI-assisted development: new models, agent frameworks, eval tooling, and emerging patterns. Run structured pilots, adopt what works, and kill what does not.
• Multiply the Team: Onboard every engineer into the platform, document the golden path, and evangelize new capabilities so that every improvement you ship compounds across the whole squad within days.

5. Security, Reliability & Cost

• Secure by Design: Build security into the platform instead of adding it at the end. That means scoped permissions and sandboxing for every agent, secrets management, dependency and supply-chain scanning, and audit trails for agent-driven changes.
• Protect Student Data: Make sure the platform handles student data, including minors’ data where it applies, in line with privacy rules such as LGPD and GDPR. Compliance should be a default in the pipeline, not a checklist.
• Own Production Health: Define SLOs, alerting and incident response for the platform. Take part in an on‑call rotation, and make sure every incident leaves the system stronger.
• Run It Like a Business: Track and optimize AWS and AI-model spend. Every dollar of infrastructure and every token should earn its place.

What We're Looking For

• Platform Builder's Track Record: Proven experience in platform engineering, developer experience, or DevOps roles in lean, high-velocity teams, ideally where you built the development infrastructure from zero rather than inheriting it.
• Advanced AI Adaptation: An active, highly opinionated practitioner of AI-assisted development. You have built agentic workflows, custom skills, and multi-agent systems with tools like Claude Code and Cursor, not just used them out of the box.
• Deep Infrastructure Fundamentals: Strong hands‑on capability across AWS (VPC, ECS/EKS, Lambda, RDS), infrastructure as code (Terraform or CDK), CI/CD architecture, and modern testing frameworks.
• Product Mindset for Internal Tools: You treat engineers as your users: you watch how they work, find the friction, and ship improvements they feel immediately. You sweat the developer experience the way others sweat the UI.
• High Ownership: Exceptional self-direction and judgment. You are deeply uncomfortable with corporate bureaucracy and thrive when given a goal and a blank canvas.
• Languages: Fluency in English is required; professional proficiency in Spanish is strongly preferred.

Strong Advantage

• Hands-on experience with LLM infrastructure: model routing, RAG and vector databases, semantic memory systems, or eval and observability frameworks for AI agents.
• Prior experience as a founding engineer or first platform hire at a startup.
• Contributions to developer tooling, open-source agent frameworks, or internal AI platforms at a previous company.