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# AI Application Architect

- **Company:** Procore
- **Location:** Bellville, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Kubernetes Security, Computer-Aided Design, Artificial Intelligence, Airflow, Amazon Web Services, Audit Trail, Microsoft Azure, Software as a Service, Continuous Integration, Cross-Site Request Forgery, Python (Programming Language), Key Management, Machine Learning, Project Management Software, Node.Js, OAuth, Open Source Technology, Productivity Software, Role-Based Access Control, OpenAI, Zero Trust Network Access, Software Engineering, Strategies of Testing, TypeScript, Management of Software Versions, Web Applications, Workflow Management Systems, Enterprise Software Applications, ReactJS, Evaluation Pipelines, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Software Security, Backend, Langfuse — LLM Observability and Analytics Platform, Rate Limiting, Pgvector, AI Platforms, Kubernetes, Milvus, Machine Learning Operations, Front End Software Development, Drift Detection, Evaluation of Large Language Models, Api Design, Google Gemini, Model Context Protocol, Human in the Loop, User Administration
- **Published:** November 5, 2026
- **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=3052392317-2

## About the Role

* 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

Preferred Experience:

 * 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

## Description

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.

What You'll Do:

 * 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