Sr. Staff AI Engineer - On-Prem AI Infrastructure & Agentic Systems

United States·San Josesenior

OtherInfrastructure

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Quick Summary

Key Responsibilities

Design and deploy on-prem AI infrastructure — including GPU clusters, model serving (e.g., vLLM, TGI, Triton), vector DBs (e.g., Milvus, Qdrant, FAISS), and orchestration (Kubernetes, Helm, Docker).

Requirements Summary

2+ years of experience in AI/ML engineering, with hands-on deployment of AI systems on-prem or private cloud. Proven experience building agentic AI systems — including state management,

Technical Tools

OtherInfrastructure

What We Offer

~1 min read

✓Build foundational AI infrastructure that powers next-gen enterprise systems.

✓Work on cutting-edge agentic AI — not just chatbots, but autonomous systems that reason, plan, and act.

✓Opportunity to influence AI strategy, deployment, and governance in a high-impact environment.

About the Role

~1 min read

We are seeking a hands-on AI Engineer to design, deploy, and maintain on-prem AI infrastructure and build agentic AI systems that drive real-world automation. You’ll be responsible for setting up scalable AI environments, implementing RAG pipelines, fine-tuning embedded models, and architecting AI agents that operate autonomously in enterprise settings. This role sits at the intersection of AI systems engineering and applied ML — you’ll bridge infrastructure, model deployment, and agent logic.

Responsibilities

~1 min read

→Experience deploying AI in enterprise storage or hardware-adjacent environments.
→Background in systems engineering or QA automation — bonus if you’ve used AI to automate testing or validation.
→Familiarity with embedded AI or edge inference (ONNX, TensorRT, GGUF, etc.).
→Experience with AI agent frameworks (LangGraph, AutoGen, BabyAGI, etc.).
→Knowledge of AI observability tools (LangSmith, Weights & Biases, Prometheus/Grafana for AI).
→As a Storage company, knowledge of storage area/NVMe is a PLUS.