> Markdown version of [/jobs/ext/3967216-ml-ai-platform-architect](https://www.wearedevelopers.com/jobs/ext/3967216-ml-ai-platform-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). --- # ML / AI Platform Architect - **Company:** OpenKyber LLC - **Location:** Atlanta, GA, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Audit Trail, Microsoft Azure, Cloud Computing, Cloud Engineering, Identity and Access Management, Python (Programming Language), Azure Machine Learning, Feature Store, Feature Engineering, Large Language Models, Agentic-AI, AI Platforms, Kubernetes, Machine Learning Operations, Software Version Control, Docker - **Published:** October 27, 2026 - **Apply:** https://www.careerjet.com/jobad/us5984e203bf69b29ee4d170a687bf9da7 ## About the Role * 7+ years of experience in AI/ML platform engineering, cloud architecture, or MLOps. * Strong experience with AI/ML platforms, Feature Stores, Model Registries/Catalogs, and MLOps frameworks. * Hands-on experience with Agentic AI, LLM platforms, and orchestration frameworks. * Proficiency in Python, APIs, Kubernetes, Docker, and cloud platforms (Azure preferred). * Knowledge of AI governance, security, compliance, and observability best practices. * Strong problem-solving, architecture, and communication skills. ## Description * Design and own the end-to-end architecture for enterprise AI/ML platforms. * Build and operationalize Feature Store and Model Catalog capabilities to support reusable feature engineering, model versioning, and self-service ML adoption. * Develop and manage Agentic AI platform infrastructure, including agent runtimes, orchestration frameworks, tool integrations, and agent lifecycle management. * Establish and maintain Agent Catalog capabilities to enable deployment, discovery, monitoring, and governance of AI agents. * Design and implement AI governance controls, including access management, policy enforcement, audit logging, monitoring, and observability. * Develop MLOps pipelines and cloud-native deployment frameworks to support scalable AI/ML workloads. * Collaborate with Data Science, Engineering, Security, and Business teams to deliver enterprise AI solutions.