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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Artificial Intelligence Architect - **Company:** Appvion, LLC - **Location:** Highland Park, IL, United States (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Automated Storage and Retrieval Systems, Microsoft Azure, Cloud Computing, Continuous Integration, Data Architecture, Information Engineering, Monitoring of Systems, Python (Programming Language), Performance Tuning, Cloud Services, Tensorflow, Azure Machine Learning, Scala (Programming Language), SQL Databases, Google Cloud, Pytorch, Large Language Models, Prompt Engineering, Apache Spark, AI Platforms, Scikit Learn, Kubernetes, HuggingFace, Machine Learning Operations, Docker - **Published:** July 27, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=dde1cd623d007554 ## About the Role * 8+ years in software or data architecture, with 4+ years focused on ML systems * Deep expertise in cloud platforms (AWS, Azure, or GCP) and their ML services * Proven experience designing production ML pipelines at enterprise scale * Strong understanding of MLOps, model monitoring, and deployment patterns * Experience with both traditional ML and modern LLM/GenAI architectures * Familiarity with core enterprise infrastructure architecture Skills * Languages: Python, SQL, and Scala for ML and data engineering * ML frameworks: PyTorch, TensorFlow, scikit-learn, and Hugging Face * MLOps: Docker, Kubernetes, CI/CD, MLflow, and model registries * Cloud & data: AWS, Azure, GCP, Spark, Airflow, and feature stores * LLM, GenAI & agentic search: RAG, fine-tuning, prompt engineering, vector databases, query planning, tool use, retrieval orchestration, and multi-step reasoning * Responsible AI: governance, model monitoring, and security by design * Solution mindset: design thinking, trade-off analysis, and pragmatic delivery ## Description We're hiring an AI Architect to define the technical foundation for all our AI/ML systems including architecture standards, platform decisions, and quality gates that let us deliver scalable, secure, and governed AI solutions tied directly to business outcomes. You'll sit at the intersection of engineering, data, and business strategy, designing the systems and setting the standards that accelerate AI adoption across the enterprise. What You'll Do * Design the enterprise AI/ML architecture, including reference patterns and multi-entity / multi-tenant architectures with governed data boundaries * Evaluate and select AI platforms, frameworks, and cloud services * Establish technical standards for model development, testing, and deployment * Design agentic search and retrieval systems for enterprise knowledge grounding * Review and approve architecture for all AI use cases before they reach production * Define data architecture requirements for ML pipelines * Lead build vs. buy evaluations for AI tooling * Mentor technical team members and drive engineering excellence * Stay current on AI/ML technology trends and assess their relevance to our roadmap ## Related Videos - [Reference Architecture of AI in the Cloud](https://www.wearedevelopers.com/videos/1613-reference-architecture-of-ai-in-the-cloud) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) ## Related Articles - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)