Principal ML Engineer — Remote (LATAM)

Ryz Labs

Argentina

Remote

ARS 272,860,000 - 378,972,000

Full time

3 days ago
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Job summary

RYZ Labs is seeking a Principal Machine Learning Engineer to join our core engineering team. You will bridge production systems, applied AI, and engineering excellence to shape our client's next-generation AI platforms, operating at the intersection of distributed systems and MLOps.

You will design scalable multi-tenant architectures, implement agent governance, and contribute hands-on across Python, Go, or TypeScript, Dockerized services, and CI/CD pipelines while collaborating with product

Qualifications

  • 12–15+ years in software engineering with a path to production ML.
  • Deep MLOps experience and AI model evaluation rubrics.
  • Strong distributed systems and microservices architecting skills.
  • Proficient in Python, Go, or TypeScript.
  • Hands-on DevOps and cloud infrastructure expertise.
  • Knowledge of LLM safety and AI governance.
  • Excellent English communication for client-facing roles.

Responsibilities

  • Deploy and manage AI models at scale, monitor performance and latency.
  • Design distributed microservices and CI/CD pipelines.
  • Implement agent permission structures, human-in-the-loop workflows, data isolation, and prompt defenses.
  • Collaborate with Product Managers and client stakeholders to translate requirements.

Skills

Distributed systems
MLOps
Production ML
Python/Go/TypeScript
Cloud & DevOps
AI governance & security
English communication

Tools

Docker
AWS
GCP
Azure
CI/CD

Job description

RYZ Labs is seeking a Principal Machine Learning Engineer to join our core engineering team. You will bridge production systems, applied AI, and engineering excellence to shape our client's next-generation AI platforms, operating at the intersection of distributed systems and MLOps.

You will design scalable multi-tenant architectures, implement agent governance, and contribute hands-on across Python, Go, or TypeScript, Dockerized services, and CI/CD pipelines while collaborating with product

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