AI Systems Architect

Qualifications

• Strong systems thinker with experience designing complex, production AI or ML systems
• Hands-on experience with LLMs, agents, or ML pipelines beyond simple API usage
• Ability to reason clearly about failure modes, edge cases, and system incentives
• Strong communication skills — you can explain why a system works, not just how
• Experience with reinforcement learning, model fine-tuning, or distillation
• Prior work on open-source infrastructure or widely used internal platforms
• Background in distributed systems, compilers, or performance-critical software
• Experience designing AI systems used by external customers at scale

Responsibilities

• You’ll help define how AI systems observe themselves, learn from real-world signals, and improve over time — with engineers staying firmly in control
• Architect end-to-end AI systems that operate in production (LLMs, agents, evaluators, optimizers)
• Design feedback loops that turn metrics, logs, and human feedback into measurable system improvements
• Define evaluation strategies for model quality, cost, latency, and regression prevention
• Lead optimization workflows such as prompt evolution, fine-tuning, model routing, distillation, or reinforcement learning
• Build or guide infrastructure for observability, experimentation (A/B testing), and automated rollouts
• Partner with product and engineering teams to translate real-world problems into scalable AI architectures
• Make principled tradeoffs between performance, reliability, cost, and speed
• Deep understanding of evaluation, experimentation, and optimization in real environments
• Comfort working across backend systems, data pipelines, and model interfaces

Full Description

About the Role

We’re building systems that don’t just use AI models — they learn, adapt, and optimize themselves in production.

We’re looking for an AI Systems Architect to design and evolve the technical backbone behind large-scale, feedback-driven AI systems.

This role sits at the intersection of LLM infrastructure, evaluation systems, optimization workflows, and production reliability.

You’llhelp define how AI systems observe themselves, learn from real-world signals, and improve over time — with engineers staying firmly in control.

This is not a research-only role and not a UI-heavy role.

It’s about system design, feedback loops, and production-grade intelligence.

What You’ll Do

• Architect end-to-end AI systems that operate in production (LLMs, agents, evaluators, optimizers).

• Design feedback loops that turn metrics, logs, and human feedback into measurable system improvements.

• Define evaluation strategies for model quality, cost, latency, and regression prevention.

• Lead optimization workflows such as prompt evolution, fine-tuning, model routing, distillation, or reinforcement learning.

• Build or guide infrastructure for observability, experimentation (A/B testing), and automated rollouts.

• Partner with product and engineering teams to translate real-world problems into scalable AI architectures.

• Make principled tradeoffs between performance, reliability, cost, and speed.

• Strong systems thinker with experience designing complex, production AI or ML systems.

• Hands-on experience with LLMs, agents, or ML pipelines beyond simple API usage.

• Deep understanding of evaluation, experimentation, and optimization in real environments.

• Comfort working across backend systems, data pipelines, and model interfaces.

• Ability to reason clearly about failure modes, edge cases, and system incentives.

• Strong communication skills — you can explain why a system works, not just how.

Nice to Have (Not Required)

• Experience with reinforcement learning, model fine-tuning, or distillation.

• Prior work on open-source infrastructure or widely used internal platforms.

• Background in distributed systems, compilers, or performance-critical software.

• Experience designing AI systems used by external customers at scale.