Solutions Architect - AI Factory Solution

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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 industrialised AI platform.

• 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.

• 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 utilisation, 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 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…