Lead AI Application Engineer (Infrastructure & LLMOps)
Tasks
- Architect and maintain multi tenant AI platform
- Build RAG as a service
- Build developer self service portal or CLI
- Build embeddings as a service
- Build inference as a service
- Create templates and blueprints to reduce time to inference
- Deploy and scale feature stores
- Deploy and scale vector databases
- Develop AI services catalogue
- Ensure high availability and low latency
- Implement LLMOps and MLOps automated deployment pipelines
- Optimize data retrieval patterns
- Oversee model hosting with Kubernetes and GPU orchestration
- Provide documentation and internal workshops
- Provision AI environments models and data stores
- Standardize unified APIs for LLM access
Perks/Benefits
- N/A
Skills/Tech-stack
AI Enterprise | Amazon Web Services | As-a-Service | Cloud platform | Developer platform | Docker | Embeddings as a Service | Feature Stores | GPU Orchestration | Go | Google Cloud | Google Cloud Platform | Inference as a Service | Internal developer platform | Kubernetes | LLMOps | MLOps | Microsoft Azure | NVIDIA AI Enterprise | NVIDIA Triton | NoSQL | Nvidia AI | On-Premises | OpenShift | Pulumi | Python | Retrieval-Augmented Generation | Rust | SQL | Terraform | Text Generation | Text generation inference | VLLM | Vector Databases | Vector Search | Web Services
Education
N/A
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