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AI Engineer
Skills
DevopsAI AgentsArtificial IntelligenceDockerKubernetesLanggraphLarge Language Models
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.
