> Markdown version of [/jobs/ext/4509877-artificial-intelligence-architect](https://www.wearedevelopers.com/jobs/ext/4509877-artificial-intelligence-architect). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Artificial Intelligence Architect - **Company:** Compu-Vision Consulting Inc. - **Location:** Austin, TX, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Microsoft Azure, Python (Programming Language), Machine Learning, Natural Language Processing, Open Source Technology, OpenAI, Microsoft Copilot, Azure Machine Learning, Enterprise Software Applications, Retrieval-Augmented Generation, Large Language Models, IT Architecture, Agentic-AI, AI Platforms, Machine Learning Operations, Databricks - **Published:** November 1, 2026 - **Apply:** https://www.juju.com/job/16_9278ca73 ## About the Role * 8 years in technology architecture/software/data roles. * 3 years designing enterprise AI/ML solutions. * Strong understanding of GenAI and modern AI architectures. * Experience with LLMs, RAG, embeddings/vector databases, model APIs, ML pipelines, and enterprise integrations. * Cloud AI platform experience. * AI security and governance knowledge. * Ability to explain AI architecture to technical and executive audiences. Highly Preferred * Azure AI Foundry / Azure AI services. * Azure OpenAI. * Microsoft Copilot ecosystem. * Databricks. * MLflow. * Python. * Responsible AI frameworks. * Agentic AI. ## Description * Develop enterprise AI and GenAI architecture. * Translate business use cases into secure, scalable AI solutions. * Architect LLM, RAG, agentic AI, NLP, predictive ML, and intelligent automation solutions where appropriate. * Design AI platform, model, data, integration, security, and deployment architecture. * Establish reusable AI architecture patterns and standards. * Evaluate commercial and open-source AI technologies. * Design integrations between AI solutions and enterprise applications/data. * Define MLOps/LLMOps architecture. * Address model security, privacy, responsible AI, explainability, monitoring, and governance. * Collaborate with enterprise architecture, data, cybersecurity, cloud, and application teams. * Conduct AI readiness assessments and develop AI roadmaps. * Support Microsoft Azure AI and Copilot-related initiatives where appropriate.