Principal AI Engineer
Principal AI Engineer at RyzLabs designs, builds, and governs AI/ML systems, tools, and standards for a startup studio, blending hands-on coding with cross-functional leadership across LATAM.
- Compensation
- Not disclosed
- Location
- Argentina
- Work setup
- Remote
- Seniority
- Principal
- Employment
- Full time
- Experience
- 15+ years
- Category
- Ai engineering
- Posted
Skills
Nice to have
- • Prior experience applying AI/ML within business consulting, advisory, professional services.
- • Hands-on experience with multi-agent systems and agent orchestration frameworks (LangGraph, AutoGen, CrewAI, DSPy, MCP).
- • Active contributions to open-source AI/ML projects, technical publications, or AI community initiatives.
- • Experience defining AI compliance controls for SOC2, ISO 27001, or TISAX frameworks.
- About RYZ Labs: RYZ Labs is a startup studio built in 2021 by two lifelong entrepreneurs. The founders of RYZ have worked at some of the world's largest tech companies and some of the most iconic cons
Full description
Location: LATAM - Remote
Type: Full-Time
About the Opportunity
We are seeking an experienced, hands-on Principal AI Engineer to drive the technical architecture and production engineering of artificial intelligence and machine learning solutions. Sitting at the intersection of software engineering, distributed systems, platform architecture, and ML engineering, you will serve as the technical owner for how AI/ML systems are designed, built, evaluated, and shipped to production.
This is a high-impact, 100% hands-on engineering and technical leadership position. You will move fluidly between writing production code, architecting multi-tenant AI services, designing MLOps and model evaluation pipelines, establishing AI governance, and guiding engineering teams.
What You Will DoAI Productization, ML Engineering & Platform Architecture
- Production ML & System Design: Design and build maintainable, scalable, and resilient production systems that integrate machine learning models, LLMs, vector databases, and agentic workflows into application architectures.
- Model Evaluation & MLOps: Establish robust model evaluation rubrics, observability pipelines, and benchmarking frameworks to monitor model performance, drift, latency, costs, and hallucination rates in production.
- AI Governance & Safety: Establish robust AI agent governance policies (permissions, code execution controls, system access, human-in-the-loop enforcement, and prompt security/data boundaries).
- Reusable Tooling & Scaffolding: Build reusable internal tooling, including AI scaffolding, monitoring systems, and data infrastructure designed for widespread developer adoption.
- Deployment & CI/CD: Define standardized deployment patterns using containerization, Infrastructure-as-Code (IaC), and reusable CI/CD pipelines to streamline service delivery.
Developer Experience & Engineering Excellence
- Engineering Standards: Codify software development standards (CI/CD, testing, delivery quality, async patterns) referenced as a baseline for new or re-platformed products.
- AI-Assisted Development: Champion AI-assisted development practices, test-driven development (TDD), and modern engineering workflows across the organization.
- Technical Mentorship: Mentor engineers and tech leads by example through code reviews, design docs, and architectural guidance.