AI Model Deployment Administrator

Experience: 12+ Location: Louisville, KY visa: Independent only AI Solution Architecture Design end to end AI/ML and GenAI architectures, covering data ingestion, model development, deployment, monitoring, and lifecycle management. Define reference architectures for LLMs, RAG, agentic workflows, predictive models, and automation use cases. Ensure scalability, reliability, performance, and cost optimization of AI platforms across cloud and hybrid environments. 2 Enterprise & Cloud Integration Integrate AI solutions with enterprise systems, APIs, data platforms, and cloud services. Align AI architecture with broader enterprise architecture, security, and DevOps standards. Guide teams on AI ready cloud patterns (containers, MLOps, CI/CD, observability). 3 Governance, Security & Compliance Work with central AI COEs and governance bodies to define guardrails, approval workflows, and audit processes for AI usage. Ensure responsible AI principles: data privacy, model explainability, bias mitigation, and regulatory compliance. Enforce security best practices including identity, access control, secrets management, and model protection. 2 Business & Stakeholder Collaboration Partner with business leaders to identify AI opportunities, define backlogs, and translate business problems into AI solutions. Provide architectural leadership during pre sales, proposals, and solution reviews. Act as a trusted advisor on AI feasibility, ROI, and risk assessment. 2 Technical Leadership & Enablement Mentor architects, data scientists, and engineers on AI design patterns and best practices. Review solution designs, conduct architecture boards, and ensure delivery quality. Contribute to internal accelerators, reusable assets, and AI capability building. 1 Required Skills & Experience Technical Skills Strong understanding of AI/ML concepts, including supervised/unsupervised learning, deep learning, NLP, and GenAI. Hands on experience with LLMs, prompt engineering, RAG architectures, vector databases, and AI APIs. Expertise in cloud platforms (Azure, AWS, or Google Cloud Platform) and AI services. Knowledge of data engineering, APIs, microservices, containers, and DevOps/MLOps. Experience with monitoring, logging, model performance tracking, and cost controls. 2 Architecture & Design Proven experience designing large scale, distributed, and secure enterprise systems. Ability to create architecture diagrams, design documents, and implementation roadmaps. Strong grasp of non functional requirements: scalability, availability, resilience, and security. 3 Experience Typically 10+ years in software/solution architecture, with 3 5+ years focused on AI/ML or data driven solutions. Experience working in regulated or large enterprise environments is preferred. (Exact years may vary based on organization standards; not explicitly defined in sources.)