Solutions Architect - AI Factory Solution

The AI Factory Solution Architect is a seasoned subject matter expert who helps customers move from isolated AI experiments to scalable, secure and sovereign AI Factory platforms. The role combines go-to-market leadership, strategic customer advisory and hands-on technical execution. It focuses on translating business use cases into production-ready AI architectures across data, model, infrastructure, security, governance and operations.

This role consults with clients and works with internal teams to create transformational designs, technical readiness assessments and architectural visions for AI Factory solutions. The architect evaluates the customer’s data readiness, cloud and datacenter infrastructure maturity, sovereignty requirements and operational readiness, then shapes a practical roadmap from proof of value to industrialized AI platform.

Key Responsibilities:

Go-to-Market Lead

• Generate market awareness for Sovereign AI, AI Factory and AI-ready datacenter propositions through customer conversations, thought leadership, sales enablement and partner collaboration.
• Become a recognized entity in the customer conversation by connecting business strategy, regulatory pressure, technology innovation and operational reality into a clear transformation narrative.
• Build compelling visions and out-of-the-box solution concepts that help customers understand how AI Factory platforms can create business value beyond a single proof of concept.
• Contribute to new go-to-market services, propositions and qualification frameworks for AI Factory opportunities, including technical readiness, business value, sovereignty and platform scalability.
• Work with sales, solution sales, delivery, alliances and product teams to shape pipeline, qualify opportunities and articulate the value of platform-based AI industrialization.

Customer Transformation & Sovereign AI Factory Architecture

• Help customers transform business use cases into practical Sovereign AI Factory setups, including the target architecture for data, compute, storage, networking, model lifecycle, governance and operations.
• Discuss sovereignty concepts with customers, including data location, data control, regulatory requirements, sensitive data handling, public AI API concerns and the trade-offs between public cloud, private cloud, hybrid cloud and dedicated AI infrastructure.
• Advise on data modelling concepts, data readiness, data fabric or mesh maturity, ingestion pipelines, data quality, lineage, privacy and integration patterns required to scale AI beyond individual pilots.
• Guide customers through tokenomics, inference economics, training cost drivers, GPU utilization, model serving patterns and the commercial implications of choosing different model strategies.
• Support LLM selection by assessing use case fit, language requirements, accuracy, latency, cost, explainability, security, licensing, openness, deployment model and operational maintainability.
• Run AI-ready datacenter assessments across compute, GPU platforms, storage, high-performance networking, cloud maturity, security, observability, MLOps, LLMOps, sustainability and operational support readiness.
• Create solution architectures, roadmaps and design views that address both business stakeholders and technical teams, including functional and non-functional requirements such as scalability, resilience, compliance, cost and performance.

Showcase, Proof of Value & Hands-On Implementation

• Showcase and implement small, relevant AI use cases in proof-of-value engagements that demonstrate the practical business and technical potential of an AI Factory platform.
• Use hands-on knowledge to support demonstrations, field trials, solution validation and early implementation activities across data ingestion, model deployment, orchestration, monitoring and integration.
• Translate proof-of-value outcomes into repeatable architectural patterns, reusable assets, customer roadmaps and clear next steps toward production-grade AI Factory adoption.
• Work closely with customer technical teams, data scientists, infrastructure teams, security teams and business sponsors to validate feasibility, remove blockers and support adoption.
• Review and improve solution designs against customer requirements, enterprise architecture standards and AI Factory principles, ensuring that pilots can evolve into scalable and supportable platforms.
• Share lessons learned, technical insights, reference architectures and emerging technology trends with internal teams to strengthen consulting quality and accelerate future engagements.

Knowledge and Attributes:

• Seasoned knowledge of AI Factory, Sovereign AI, hybrid cloud, private cloud, datacenter infrastructure and enterprise architecture concepts.
• Strong understanding of MLOps, LLMOps, model lifecycle management, data pipelines, observability, security, governance and operational support models.
• Ability to bridge executive-level business conversations and deep technical discussions with architects, data scientists, infrastructure teams and security stakeholders.
• Strong communication and storytelling skills, with the ability to build awareness, shape vision and make complex AI architecture concepts understandable and actionable.
• Ability to evaluate emerging AI technologies, LLM ecosystems, open-source models, commercial model platforms, GPU infrastructure and AI software stacks.
• Consultative mindset with the ability to challenge assumptions, propose creative alternatives and guide customers from ambition to executable roadmap.
• Hands-on curiosity and practical technical drive to demonstrate, test and validate small use cases that prove value and reduce customer uncertainty.

Academic Qualifications and Certifications:

• Bachelor’s degree or equivalent experience in computer science, engineering, data science, business technology or a related field.
• Relevant certifications in enterprise architecture, cloud architecture, AI, data engineering, security, Kubernetes, MLOps or IT service management are advantageous.
• Working knowledge of enterprise architecture methodologies and technology governance frameworks is preferred.

Required Experience:

• Seasoned professional experience in technical consulting, solution architecture, infrastructure architecture, AI architecture, data platform architecture or technology services.
• Experience designing, selling or delivering complex solutions across cloud, datacenter, storage, networking, security, data platforms or AI infrastructure.
• Experience working with customers on needs assessment, business case development, technical readiness, transformation roadmaps and change management.
• Experience with proof of concept or proof of value delivery, including translating technical outcomes into business value and scalable next steps.
• Experience collaborating across sales, delivery, product, partner, data, security and infrastructure teams in a complex enterprise environment.

Experience with agile delivery, architecture governance, design reviews and the transition from prototype to production is preferred.