Platform Engineer - Cloud, AI-Native & Scale (Remote)

AgileEngine, LLC.; Municipio de Rincón de los Sauces; Hybrid; ARS 169,108,000 - 230,602,000; Full time

Benefits offered by this job

Professional growth

Competitive compensation

Exciting projects

Flextime

Job summary

AgileEngine is seeking a Platform Engineer to design and scale core infrastructure that supports rapid growth. You will work on cloud resources with AWS and Kubernetes, and help keep systems secure and performant.

You will collaborate with engineering and data science teams, implement AI-native tooling, and contribute to scalable platform services with a distributed architecture mindset.

Qualifications

• 3+ years of server development in Python or TypeScript.
• 1+ year API design and development using REST/gRPC.
• 1+ year AWS and Kubernetes experience, incl. Istio/Linkerd/Cilium service mesh.
• 1+ year building architectural components (service registry, streaming, caching, etc.).
• 6+ months work with LLMs, prompt engineering or AIOps tooling.
• 6+ months SDLC AI-native usage via Claude Code or Claude Cowork.
• Excellent communication skills, willingness to learn, ownership.
• Upper-intermediate English level.

Responsibilities

• Design, build, and maintain scalable platform services to support growth.
• Measure, monitor, and optimize system performance; fix bottlenecks.
• Manage cloud resources and service boundaries for security and ops.
• Implement best practices to optimize development processes.
• Communicate status, risks, and issues with leadership.

Skills

Python

TypeScript

API design

Cloud architecture

LLMs & AI tooling

Ownership & communication

English proficiency

Tools

AWS

Kubernetes

Service Mesh

Istio/Linkerd/Cilium

Claude Code

Claude Cowork

Job description

AgileEngine is seeking a Platform Engineer to design and scale core infrastructure that supports rapid growth. You will work on cloud resources with AWS and Kubernetes, and help keep systems secure and performant.

You will collaborate with engineering and data science teams, implement AI-native tooling, and contribute to scalable platform services with a distributed architecture mindset.