AI Platform Architect — LLM Infrastructure & Scale

DroneDeploy, Inc.; Austin (TX); On-site; USD 233,000 - 321,000; Full time

Benefits offered by this job

Equity compensation

Bonus incentives

Job summary

Procore is seeking an experienced AI Application Architect to lead the technical design and evolution of our AI-powered product suite. You will be the technical authority on how we build, scale, and secure AI-native applications — spanning agent orchestration, LLM infrastructure, data pipelines, and developer tooling.

You will collaborate with engineering leads, product managers, and ML practitioners to translate ambitious AI capabilities into robust, production-grade systems, while mentoring

Qualifications

• 10+ years in software engineering at architect level.
• Hands-on experience shipping production LLM-powered apps.
• Experience delivering SaaS/enterprise platforms.
• Strong frontend (React/TS) and backend (Python) with API design.
• Knowledge of RBAC/ABAC, audit logging and compliance.

Responsibilities

• Define architecture for AI-native apps (agents, RAG, multi-model).
• Lead scalable AI workloads with vector DBs and orchestration tools.
• Design LLM integration, prompts, context windows, and cost governance.
• Lead LLMOps and observability with tracing, evaluation pipelines, and drift detection.
• Establish security and compliance posture for AI systems and supply chain.
• Mentor engineers and promote ADRs and engineering standards.

Skills

Software architecture

Team leadership

LLM applications

Full-stack

API security

Cloud-native

Tools

Turbopuffer

pgvector

Milvus

Temporal

Airflow

OpenTelemetry

Langfuse

Arize

Kubernetes

Helm

Job description

Procore is seeking an experienced AI Application Architect to lead the technical design and evolution of our AI-powered product suite. You will be the technical authority on how we build, scale, and secure AI-native applications — spanning agent orchestration, LLM infrastructure, data pipelines, and developer tooling.

You will collaborate with engineering leads, product managers, and ML practitioners to translate ambitious AI capabilities into robust, production-grade systems, while mentoring