Principal ML Engineer

Ryz Labs

. Deploy and manage AI models at scale.

Posted 10/9/2026full-timeRemote • ArgentinaLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in deploying and managing AI models at scale, with a strong focus on MLOps, distributed systems, and microservices architecture. Proficient in translating business requirements into scalable solutions while effectively communicating complex technical concepts to diverse stakeholders.

Highest-signal resume keywords

MLOpsDistributed Systems ArchitectureMicroservices DevelopmentCI/CD PipelinesCloud Infrastructure (AWS/GCP/Azure)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonGoTypeScriptModel Evaluation RubricsVector SearchFine-TuningDockerEvent-Driven PatternsLLM SafetyData Boundary Enforcement

Soft Skills

Clear CommunicationStakeholder Engagement

Tools & Technologies

Infrastructure-as-CodeMulti-Agent Orchestration FrameworksHuman-in-the-Loop Workflows

Industry Keywords

AI PlatformsProduction ML EngineeringAsynchronous PatternsThreat ModelingAgent Security

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsDockerGoogle Cloud PlatformMicroservicesPythonTypeScriptGo

About the role

Key responsibilities & impact

Deploy and manage AI models at scale.
Monitor model performance, hallucination rates, drift, latency, and infrastructure costs.
Design distributed, event-driven microservices using Python, Go, or TypeScript.
Build infrastructure-as-code and CI/CD pipelines to ship services.
Implement agent permission structures, human-in-the-loop workflows, data isolation boundaries, and prompt injection defenses.
Partner with Product Managers from inception to translate business requirements into scalable architectures.
Present architectural trade-offs to executives and key client stakeholders.
Shape next-generation AI platforms for an international client.
Serve as a trusted technical authority across production systems, applied AI, and engineering excellence.

Requirements

What you’ll need

12–15+ years in software engineering, with a clear evolution from Backend/Distributed Systems Architecture into applied Production ML Engineering.
Deep experience with MLOps, model evaluation rubrics, advanced RAG, vector search (embeddings, HNSW, hybrid search), and fine-tuning.
Strong mastery of distributed systems, microservices, and asynchronous event-driven patterns in Python, Go, or TypeScript.
Hands-on command of Docker, cloud infrastructure (AWS/GCP/Azure), and automated CI/CD pipelines.
Practical knowledge of LLM safety, threat modeling, data boundary enforcement, and agent security.
Fluent English.
Ability to articulate complex technical trade-offs clearly to client executives and non-technical stakeholders.
Prior experience with multi-agent orchestration frameworks (e.g., LlamaIndex, Semantic Kernel, AutoGen, CrewAI, MCP) is nice-to-have.
Experience in fast-paced consulting, advisory, or high-growth tech platforms is nice-to-have.