AI Application Architect

Procore Technologies•Austin, TX

1d•$233,360 - $320,870

About The Position

We are looking for 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'll work closely with engineering leads, product managers, and ML practitioners to translate ambitious AI capabilities into robust, production-grade systems.

Requirements

10+ years of software engineering with 5+ years in a principal/staff or architect-level role
Hands-on experience designing and shipping production LLM-powered applications (not just prototypes)
Experience shipping complex, user-facing application platforms — particularly in SaaS, productivity tools, or enterprise software.
Demonstrated ability to lead full-stack teams building rich web application experiences (React, TypeScript/Node.js, modern frontend architectures) with strong backend (Python) and API design sensibility
Deep understanding of enterprise requirements: User Management, RBAC/ABAC permission models, audit logging, compliance frameworks, multi-tenant governance, and admin tooling.
Experience building contextual or adaptive UX — applications that respond to user state, workflow context, or personalization signals.
Strong grasp of cloud-native architecture (Kubernetes, Helm, container security, CI/CD) on AWS, GCP, or Azure
Solid understanding of API security: OAuth2, CSRF, rate limiting, secrets management, and zero-trust principles applied to AI endpoints

Nice To Haves

Experience with MCP (Model Context Protocol) or comparable tool-calling/plugin infrastructure
Familiarity with workflow orchestration engines (Temporal, Prefect) for long-running AI tasks
Exposure to LLM evaluation frameworks (automated judging, red-teaming, regression suites)
Background in developer-facing products or internal AI platforms / AI coding tooling
Understanding of supply chain security for ML models (artifact signing, registry enforcement, SBOM)
Contributions to open-source AI tooling or published architectural writing

Responsibilities

Define architecture for AI-native applications including agentic systems, RAG pipelines, multi-model inference layers, and human-in-the-loop workflows
Drive infrastructure decisions for scalable AI workloads: vector databases (Turbopuffer, pgvector, Milvus), workflow orchestration (Temporal, Airflow), and async compute patterns
Design and govern the integration layer between LLMs (OpenAI, Anthropic, Gemini, open-source) and backend services, including prompt management, context window optimization, and cost governance
Lead LLMOps and observability strategy — tracing (OpenTelemetry), evaluation pipelines, prompt versioning, drift detection, and integration with platforms like Langfuse or Arize
Establish security and compliance posture for AI systems — SSRF/injection hardening, LLM guardrails, data residency, and supply chain security for model artifacts and dependencies
Partner with product and research to evaluate emerging AI capabilities and determine when/how to adopt them (e.g., reasoning models, multimodal, fine-tuning, RLHF)
Champion engineering standards — API design, schema governance, testing strategies, and architecture decision records (ADRs)
Mentor senior engineers and establish guild-level technical communities around AI platform topics