Senior AI Engineer - AI Platform

ClickUp ClickUp · Enterprise · United States · Engineering

Senior AI Engineer focused on building the core AI platform and applying LLMs to deliver intelligent features across ClickUp. Responsibilities include architecting scalable AI platform services, applying LLMs to product features, building backend systems and APIs, developing infrastructure for model serving and evaluation, integrating with LLM providers, and optimizing platform performance. Requires extensive experience in AI/ML platforms, backend engineering, distributed systems, and MLOps.

What you'd actually do

  1. Architect, design, and implement scalable AI platform services that support the deployment, orchestration, and lifecycle management of LLMs and other AI models.
  2. Apply LLMs and other AI technologies directly to build and enhance ClickUp’s intelligent features, working closely with product and engineering teams to deliver impactful solutions.
  3. Build and maintain robust APIs and backend systems that enable seamless integration of AI-powered features into ClickUp’s core platform.
  4. Develop infrastructure for model serving, monitoring, logging, and automated evaluation to ensure high reliability and performance of AI services in production.
  5. Integrate with multiple LLM providers (e.g., OpenAI, Anthropic, Google) and manage model selection, routing, and fallback strategies for optimal performance and cost.

Skills

Required

  • Python
  • Go
  • TypeScript
  • Kubernetes
  • Docker
  • AWS
  • GCP
  • Azure
  • LangGraph
  • Airflow
  • Kubeflow
  • Ray

Nice to have

  • Search technologies

What the JD emphasized

  • Extensive experience designing and building scalable AI/ML platforms or infrastructure in a production environment.
  • Proven track record of applying LLMs and AI models to real-world product features and user-facing solutions.
  • Deep expertise in backend engineering, distributed systems, and cloud-native technologies (e.g., Kubernetes, Docker, AWS/GCP/Azure).
  • Proven experience integrating and managing multiple LLMs and AI models, with a strong understanding of their operational requirements and limitations.
  • Proficiency in orchestration frameworks and workflow engines (e.g., LangGraph, Airflow, Kubeflow, Ray, or similar).
  • Demonstrated ability to address AI privacy and security challenges, including data anonymization and compliance with data protection regulations.

Other signals

  • building core AI platform
  • applying LLMs to deliver intelligent features
  • scalable AI platform services
  • model serving, monitoring, logging, and automated evaluation
  • integrate with multiple LLM providers