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About This Role
Mativ is a global leader in specialty materials headquartered in Alpharetta, Georgia. The Company offers a wide range of critical components and engineered solutions that connect, protect, and purify our world.
POSITION TITLE: AI Architect
LOCATION: Alpharetta or Remote
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.
DESCRIPTION
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.
RESPONSIBILITIES
AI Strategy Enablement & Alignment:
- 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.
AI Solution Architecture & Design: 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.
AI/ML Governance & Lifecycle Management
- 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.
Performance, Scalability & Reliability
- 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.
QUALIFICATIONS
Required
- 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.
Preferred
- 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.
Competencies
- 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.
The annual salary range for this position is $120,000- $140,000. This role is also short-term incentive plan eligible. This posting will close within 90 days of the date posted. We encourage you to apply now!
WHAT WE OFFER
Mativ benefits say a lot about how we care for each other. Our employees and their families have many different needs. As a result, our benefits offer choices on many levels and are high in quality, competitive in the marketplace, and affordable.
ABOUT MATIV
Mativ Holdings, Inc. is a global leader in specialty materials headquartered in Alpharetta, Georgia. The company connects, protects, and purifies the world every day through a wide range of critical components and engineered solutions that solve our customers’ most complex challenges. We manufacture on three continents and generate sales in nearly 100 countries through our family of business-to-business and consumer product brands. The company’s two segments, Filtration & Advanced Materials and Sustainable & Adhesive Solutions, target premium applications across diversified and growing end-markets, from filtration to healthcare to sustainable packaging and more. Our broad portfolio of technologies combines polymers, fibers, and resins to optimize the performance of our customers’ products across multiple stages of the value chain. Our leading positions are a testament to our best-in-class global manufacturing, supply chain, and materials science capabilities. We drive innovation and enhance performance, finding potential in the impossible.
Salary Context
This $120K-$140K range is in the lower quartile for AI Architect roles in our dataset (median: $175K across 37 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 9,079 AI roles we're tracking, AI Architect positions make up 1% of the market. At Mativ, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $224,070 based on 236 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($130K) sits 42% below the category median. Disclosed range: $120K to $140K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Mativ AI Hiring
Mativ has 1 open AI role right now. They're hiring across AI Architect. Based in Alpharetta, GA, US. Compensation range: $140K - $140K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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