POSITION OVERVIEW
As a pivotal member of Ashley's Data & AI Team, the Chief AI Architect is the principal technical leader responsible for designing, scaling, and governing the AI architecture that powers the organization's AI-driven initiatives. This role bridges strategic AI vision with practical, enterprise-grade implementation. The Chief AI Architect defines the technical roadmap, selects and standardizes AI platforms and tools, and ensures cohesive integration with the broader IT and data ecosystem.
This role requires deep technical expertise in machine learning, generative AI, data engineering, and cloud infrastructure, along with strong collaboration skills to align with data science, engineering, cybersecurity, compliance, and business teams. The Chief AI Architect plays a critical role in ensuring AI solutions are scalable, secure, ethical, and aligned with business outcomes.
KEY RESPONSIBILITIES
AI Architecture Strategy & Design (30%)
- Define and maintain the enterprise AI architecture roadmap in alignment with the Organization's strategic vision for AI.
- Lead the design of scalable, modular, and secure AI platforms and services.
- Select and standardize AI/ML frameworks, ML Ops tools, and model deployment patterns.
Technical Oversight & Solution Review (25%)
- Review and guide the architecture of AI models, pipelines, and services developed by engineering and data science teams.
- Ensure reusability, performance optimization, and compliance with architectural standards.
- Act as final escalation point for technical AI decisions.
Cross-Functional Collaboration (20%)
- Collaborate with technical and business teams to align AI architecture with enterprise priorities.
- Partner with engineering, product, and data teams to embed AI solutions into business platforms.
- Communicate technical concepts clearly to non-technical stakeholders.
Innovation & Technology Scouting (15%)
- Stay ahead of emerging AI/ML technologies and assess applicability to enterprise needs.
- Lead proofs of concept (PoCs) for advanced AI capabilities, including generative AI and foundation models.
- Contribute to the organization's AI innovation pipeline in partnership with R&D or innovation teams.
Governance, Security & Responsible AI (10%)
- Establish architectural standards for responsible and ethical AI, including model transparency and auditing.
- Ensure compliance with data privacy, security, and regulatory requirements in AI design.
- Support the integration of monitoring, guardrails, and risk controls into AI systems.
REQUIRED QUALIFICATIONS
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related technical field; Ph.D. is a plus.
- 12+ years of experience in software engineering, data science, or AI/ML roles, with at least 5 years in an architectural or technical leadership capacity.
- Deep expertise in AI/ML architecture, including model design, training pipelines, MLOps, and deployment at scale.
- Proven experience architecting and delivering enterprise-grade AI solutions in cloud environments (AWS, Azure, GCP).
- Strong understanding of data engineering, data governance, and scalable data platforms (e.g., data lakes, feature stores).
- Familiarity with generative AI, large language models, foundation models, and their practical integration into business applications.
- Working knowledge of AI governance, ethics, and responsible AI practices, including model auditability and bias mitigation.
- Hands-on experience with modern AI/ML frameworks and tools (e.g., TensorFlow, PyTorch, MLflow, Kubeflow, LangChain).
- Strong Communication and stakeholder engagement skills, with the ability to translate complex technical concepts into business-aligned strategies.
TRAVEL REQUIREMENTS
- 20% domestic and international travel required, including regular visits to teams and facilities across the United States and Asia.
IMPACT METRICS
- Reduction in time-to-deploy for AI models and services through standardized architecture and MLOps frameworks.
- Percentage of AI models, pipelines, or components reused across multiple business units or solutions.
- Measurable improvement (e.g., % reduction in manual effort, processing time, or errors) in key workflows enabled by embedded AI solutions.
- Quantified value (e.g., increased revenue, cost savings, or margin improvement) attributed to AI-powered enhancements in core systems (e.g., CRM, ERP, supply chain).