Senior Cloud Infrastructure Engineer, AI Platform

Procore Technologies•Austin, TX

4d•$140,960 - $193,820•Hybrid

About The Position

We are hiring a Senior Cloud Infrastructure Engineer to build the core infrastructure that powers the next generation of AI agents. Our agents must ingest, enrich, and vectorize massive, multi-modal datasets from over 100 customer sources. The core challenge is twofold: how do we do this in a way that is radically cost-efficient, while still allowing agents to deliver thorough responses in seconds? This position reports to a Senior Manager, Software Engineering and will be 2 days per week hybrid role in our Austin office. We’re looking for someone to join us immediately.

Requirements

5+ years of hands-on experience with cloud platforms
Hands-on experience with cloud platforms, with strong expertise in Google Cloud Platform (GCP) and Infrastructure as Code (e.g., Terraform)
A demonstrated history of performance analysis, tuning, and infrastructure cost optimization, with the ability to speak about trade-offs and quantified impact
Experience building or working on multi-tenant SaaS platforms
Experience setting up end-to-end observability, including logging, metrics, and alerting using tools like Prometheus, Grafana, Datadog, or GCP Operations suite

Nice To Haves

A fundamental understanding of the challenges in training and serving large machine learning models (e.g., memory constraints, computational complexity)
Strong understanding of VectorDBs, LLMs, and Agentic Observability tools (Datagrid uses Milvus for our VectorDB and Arize for agent tracing)
Experience with the Gemini API, specifically managing LLM quotas and load balancing
Hands-on experience with LLM serving frameworks and optimization techniques (quantization, tensor parallelism, FlashAttention)
Experience designing multi-tenant SaaS architectures and implementing resource quotas and cost allocation
High-level knowledge of agentic systems and best practices

Responsibilities

Build & Optimize for Scale & Cost: Implement and optimize our highly scalable, multi-tenant data ingestion and vectorization pipeline, with a relentless focus on improving cost and performance.
Implement Robust Monitoring: Create and maintain dashboards, alerts, and logging to ensure system health, identify performance bottlenecks, and provide immediate visibility into production issues.
Contribute to Millisecond Latency: Be a key contributor in performance tuning across the stack. You will help identify and eliminate bottlenecks in data retrieval, model inference, and agent response times to ensure a snappy, real-time user experience.
Engineer Multi-Tenant Architectures: Design and implement scalable, secure, and cost-effective multi-tenant infrastructures for our SaaS-based AI products, ensuring strict tenant isolation and fair resource allocation.