LLM Architect

Giotto.ai

17h•Hybrid

About The Position

Giotto.ai is a Switzerland-based AI company building intelligence systems for Switzerland and Europe. Our mission is to build AI capabilities that enable Switzerland and Europe to preserve strategic independence, cultural identity, and core values while achieving world-class performance in advanced reasoning systems. Our research has demonstrated strong performance on international benchmarks such as ARC-AGI. We are now building the next generation of production AI systems focused on reasoning, efficiency, and scalable deployment. The Role We are looking for a senior engineer to help architect and build large-scale AI systems around LLMs, reasoning models, and distributed inference infrastructure. You will work on core AI architecture problems: scalable inference systems distributed training pipelines reasoning-oriented model architectures efficient serving and orchestration production systems for advanced AI workloads Depending on your profile, you may operate as: a highly autonomous senior individual contributor a technical lead for a core AI initiative or an architect helping shape the long-term direction of our AI stack We value people who combine deep technical judgment with strong execution.

Requirements

5+ years building ML/NLP systems
Strong expertise in Python and PyTorch
Proven experience deploying ML systems into production
Deep understanding of transformer architectures and modern LLM systems
Experience with distributed compute environments
Comfortable operating in fast-moving research + production settings
Strong ownership mindset and ability to work from first principles
Candidates who have built difficult systems end-to-end and can speak concretely about trade-offs, failures, scaling challenges, and engineering decisions.

Nice To Haves

PhD in Computer Science, Mathematics, Physics, or related hard sciences
Experience at top-tier AI labs or large-scale technology companies
Background in systems optimization or infrastructure engineering
Competitive programming or Olympiad background (IMO, IOI, IPhO, etc.)
Open-source contributions in ML infrastructure or LLM tooling

Responsibilities

Architecting production-grade LLM systems
Designing scalable inference and training infrastructure
Optimizing performance across GPU and distributed environments
Building systems around reasoning and agentic workflows
Improving efficiency, latency, reliability, and throughput
Working closely with research teams to bring frontier ideas into production
Contributing to the long-term technical direction of sovereign AI systems in Europe