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AI Engineer

Skills
DevopsAI AgentsArtificial IntelligenceDockerKubernetesLanggraphLarge Language Models
Role

What the job involves

The main requirements, responsibilities and hiring steps.

Requirements

  • 5+ years shipping production software including applied AI or ML work
  • Experience running and optimizing self-hosted LLMs on dedicated multi-GPU hardware
  • Hands-on with a serving stack such as vLLM SGLang or TensorRT-LLM
  • Experience with tensor parallelism quantization batching and KV cache optimization
  • Track record of improving inference latency throughput and GPU utilization
  • Strong Python and engineering fundamentals
  • Ability to build quick UI and work across the app layer
  • Hands-on with agent frameworks LLM APIs embeddings and RAG
  • Comfortable with AWS Docker CI/CD monitoring and observability
  • Experience building internal tooling or platforms others depend on

Nice to have

  • Performance-minded
  • Rapid prototyper
  • AI-first builder
  • Collaborative
  • Purpose-driven

Day to day

  • Run and optimize a self-hosted inference stack on dedicated GPU hardware, tuning serving systems for high throughput and low latency.
  • Build the internal AI platform and observability layer that employees rely on, making performance and usage easy to understand.
  • Ship user-facing AI agents and product features that help families coordinate, summarize, draft, schedule, and take action inside the apps.