AI/LLM Technology Architecture

About the Role

Design and deliver full-stack AI architecture for AI platforms, products, and enterprise solutions. As an AI Architect, you will be responsible for creating scalable, secure, and governance-compliant AI foundations that enable enterprise-wide AI adoption. This role requires deep technical expertise combined with strategic vision and the ability to influence senior stakeholders.

Key Responsibilities

• Architect and implement scalable AI and machine learning models and systems that align with business goals
• Oversee the integration of AI models into existing systems, ensuring seamless deployment and functionality
• Evaluate and select appropriate AI tools, technologies, and frameworks to meet project requirements
• Design data pipelines and workflows for efficient data collection, preprocessing, and storage
• Work closely with data scientists, engineers, and stakeholders to define AI architecture strategies
• Optimize AI models and systems for performance, scalability, and reliability
• Ensure AI systems comply with security standards and regulatory requirements
• Create and maintain comprehensive documentation of AI architecture, design decisions, and workflows
• Stay up-to-date with the latest AI trends and technologies, applying innovative solutions to business problems
• Provide guidance and mentorship to junior team members on AI architecture best practices
• Engage with clients to understand business needs and provide expert guidance on AI strategy and implementation
• Build and maintain strong client relationships, acting as a trusted advisor for AI initiatives
• Support business development by identifying AI opportunities and contributing to proposal development

Required Skills & Knowledge

• Architecture: Enterprise architecture, solution architecture, and technical architecture patterns
• AI/ML: Deep understanding of ML lifecycle, model deployment patterns, and AI system design
• LLMs: Strong knowledge of LLM technologies, their differences, and modalities
• Cloud: Expert-level in at least one cloud provider; multi-cloud architecture experience preferred
• Infrastructure: Kubernetes, containerization, GPU clusters, model serving infrastructure
• Security: AI security, model governance, data privacy, compliance frameworks
• Governance: Responsible AI principles, model risk management, AI ethics
• Stakeholder Management: Experience presenting to and influencing senior executives

Preferred Qualifications

• Experience with agentic AI architectures and autonomous system design
• Knowledge of event-driven architectures and real-time AI systems
• Experience with data mesh and modern data architecture patterns
• TOGAF or similar enterprise architecture certification
• Experience in a consulting or client-facing role
• Thought leadership through publications, speaking engagements, or open-source contributions
• Knowledge of AI Native and cloud platforms (OpenAI, Anthropic, AWS, Azure, Google Cloud)