Senior Fullstack Engineer - Ai/ml

ClickHouse ClickHouse · Data AI · Cloud Engineering

Senior Full Stack Engineer to develop AI/ML-powered features in ClickHouse Cloud, bridging database technology with AI capabilities. Responsibilities include implementing AI/ML integrations, end-to-end solutions from inference API to UI, and enhancing user interaction with data.

What you'd actually do

  1. Implement and maintain integrations with the broader AI/ML ecosystem and standards, ensuring that ClickHouse works seamlessly with popular frameworks and tools.
  2. Design and implement AI-powered features across the full stack, from backend inference services to intuitive frontend interfaces within the ClickHouse Cloud platform.
  3. Create robust, scalable APIs that connect ClickHouse's database capabilities with modern AI/ML inference systems and external AI services.
  4. Build responsive, intuitive user interfaces that make complex AI functionalities accessible and valuable to users of all technical backgrounds.
  5. Work closely with the AI/ML team to integrate models into production systems with proper monitoring, versioning, and observability.

Skills

Required

  • 5+ years of full-stack development experience
  • TypeScript/JavaScript and React
  • Go, Python, or TypeScript
  • API design and service architecture
  • integrating and deploying AI/ML models in production systems
  • working with inference APIs
  • vector databases
  • cloud technologies (AWS, Azure, or GCP)
  • containerization and orchestration technologies (Docker, Kubernetes)

Nice to have

  • ClickHouse experience
  • data-oriented interfaces and visualizations
  • developer tools, SDKs, and documentation

What the JD emphasized

  • at least 2 years working directly with AI/ML technologies in production environments
  • integrating and deploying AI/ML models in production systems
  • working with inference APIs
  • vector databases
  • cloud technologies such as AWS, Azure, or GCP, particularly services related to AI/ML deployment
  • high level of ownership
  • drive features from concept to production with minimal supervision

Other signals

  • integrating and deploying AI/ML models in production systems
  • working with inference APIs
  • vector databases
  • cloud technologies such as AWS, Azure, or GCP, particularly services related to AI/ML deployment