AI Architect

Mativ•Alpharetta, GA

2d•Hybrid

About The Position

The AI Architect designs scalable AI solutions that deliver measurable business value while ensuring the right platforms, architecture, and governance are in place to support effective and responsible AI adoption. We are seeking a dynamic AI Architect to lead enterprise AI initiatives and drive the integration of artificial intelligence across the organization. This hands-on role is responsible for designing AI systems from the ground up, partnering with cross-functional teams to develop solutions aligned with specific business needs while ensuring scalability and long-term sustainability. The AI Architect will collaborate closely with the AI delivery leader and delivery team, Enterprise Architecture, and other teams as needed to ensure solution designs are practical, aligned, and successfully delivered. In parallel, the role will oversee vendor platforms, ensuring effective AI-related integrations, optimal performance, and alignment with enterprise architecture standards. This position will play a key role in helping Mativ become a frontier AI-driven enterprise by accelerating responsible AI adoption, embedding intelligent capabilities into core business processes, and enabling faster, data-driven decision-making that strengthens innovation and competitive advantage.

Requirements

10+ years of experience in data, analytics, AI, software, or enterprise architecture roles, including experience leading enterprise-scale technology initiatives.
Proven experience designing and deploying AI solutions that drive measurable business value.
Experience architecting Generative AI, machine learning, AI agent, Copilot, and intelligent automation solutions.
Experience establishing AI governance frameworks, Responsible AI practices, model lifecycle management, and compliance controls.
Experience defining enterprise AI architectures, technical standards, reference architectures, and reusable design patterns.
Experience establishing or governing MLOps and LLMOps capabilities including deployment, monitoring, observability, versioning, and continuous improvement.
Strong understanding of cloud AI platforms including Azure AI, Azure OpenAI, Databricks, Microsoft Copilot, and related technologies.
Experience evaluating build-versus-buy decisions, vendor platforms, and AI technology roadmaps.
Excellent communication, stakeholder management, and executive presentation skills.
Strong analytical thinking, architecture leadership, and problem-solving capabilities.
Demonstrates deep expertise in AI platforms, cloud technologies, data architecture, MLOps/LLMOps, and emerging AI capabilities to support enterprise-scale deployment and adoption.
Evaluates emerging AI technologies, vendor solutions, and industry trends to identify opportunities that accelerate innovation and competitive advantage.
Translates business challenges into AI-enabled opportunities, develops value-based recommendations, and balances investment, risk, scalability, and expected outcomes.
Effectively influences and advises senior leaders, business stakeholders, and technical teams by translating complex technical concepts into actionable business decisions.
Provides thought leadership, mentorship, and architectural guidance while building organizational AI capabilities, standards, and best practices.

Nice To Haves

Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
Experience leading enterprise AI transformation initiatives.
Experience with AI observability, model performance management, and operational reliability practices.
Microsoft Azure AI, Databricks, TOGAF, Cloud Architecture, or AI-related certifications.

Responsibilities

Execute the enterprise AI strategy by translating priorities into actionable initiatives, best-fit architectures, delivery plans, and measurable business outcomes.
Translate business problems into AI-enabled solutions with clear ROI.
Assess the technical feasibility, architecture options, platform fit, and technical risks of prioritized AI use cases; provide recommendations to the AI portfolio and delivery prioritization process.
Establish and maintain the enterprise AI technology and capability roadmap, including platforms, architecture patterns, shared services, technical enablers, and capability sequencing in partnership with cross-functional AI leaders.
Stay current on AI trends and emerging technologies.
Design end-to-end AI architectures (platform → data → model → application → integration)
Select the right approaches (ML, GenAI, NLP, computer vision, etc.)
Define cloud, hybrid, and edge platform architectures for AI use cases.
Ensure modular, reusable, and scalable design patterns.
Define and maintain technical AI architecture and lifecycle standards.
Provide architecture approval and technical exception recommendations through established governance processes.
Establish enterprise architectures and standards for model development, testing, and deployment across AI platforms.
Implement governance controls for versioning, traceability, auditability, and reproducibility.
Embed responsible AI practices, including privacy, bias mitigation, and compliance.
Establish and govern enterprise MLOps/LLMOps practices, including model deployment, observability, performance monitoring, drift detection, traceability, lifecycle management, and continuous improvement.
Establish AI observability to monitor model performance, data quality, pipelines, and usage with proactive alerting.
Define KPIs to measure model effectiveness, operational health, and business impact.
Guide build vs. buy vs. extend decisions to balance speed, cost, and scalability.
Partner with data, platform, and security teams to align AI governance with enterprise architecture.
Define and manage operational SLAs for AI solutions (latency, availability, throughput)
Architect for scale and cost (distributed compute, GPU optimization, inference efficiency)
Monitor and optimize operational performance.