We are recruiting an AI Solutions Architect to lead the design and delivery of enterprise-grade AI and Generative AI solutions across cloud platforms, with a strong emphasis on production deployment, business value and consulting-led delivery.
This role sits at the intersection of:
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Solution architecture (end-to-end systems design)
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AI engineering (capability awareness, not hands-on build ownership)
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Consulting (client engagement, commercial alignment, pre-sales)
The successful candidate will translate complex business problems into scalable AI architectures, lead multidisciplinary teams, and ensure AI solutions are aligned to enterprise systems, governance, and measurable outcomes.
Role Context & Positioning:
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Senior member of the AI & Data capability working across multiple client engagements
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Acts as the bridge between AI engineering, architecture, and business stakeholders
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Owns solution design, architecture governance and delivery oversight
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Plays a key role in pre-sales, client shaping, and capability development
Responsibilities:
AI Solution Architecture & Design
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Lead the design of end-to-end AI architectures across data, application and integration layers
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Design solutions spanning:
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Generative AI (LLMs, RAG, agents)
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Document intelligence and automation
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Enterprise AI platforms and APIs
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Define:
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Data flow, integration patterns, and system architecture
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Retrieval, orchestration and agent interaction patterns
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Security, governance and deployment architectures
Client Advisory & Solution Shaping
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Lead discovery workshops and use case definition sessions
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Translate business problems into AI-enabled solutions and architecture blueprints
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Advise clients on:
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AI adoption roadmaps
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Architecture approaches (build vs buy vs hybrid)
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Trade-offs, risks, and ROI
Delivery Leadership
Own architecture across delivery lifecycle:
Discovery design build oversight deployment
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Guide engineering teams on:
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Architecture decisions
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Design patterns and best practice
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Ensure:
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Production-grade delivery
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Alignment to enterprise systems and constraints
Cloud AI Architecture
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Architect solutions across at least one hyperscaler (Azure preferred), including:
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Azure OpenAI, AI Foundry, AI Search, Document Intelligence
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Equivalent AWS (Bedrock) or GCP (Vertex AI) services
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Define:
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Deployment patterns (APIs, microservices, serverless)
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Integration into enterprise ecosystems
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Security, networking and governance models
Data & AI Platform Design
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Design data foundations required for AI:
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Data pipelines, ingestion patterns, storage and modelling
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Vector databases, embeddings and retrieval strategies
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Ensure:
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Data quality, lineage, and governance alignment
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AI-readiness of enterprise data platforms
Pre-Sales & Commercial Contribution
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Support and lead:
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Solution design for proposals and RFPs
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Estimation, costing and effort modelling
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Contribute to:
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Client pitches and demos
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Opportunity shaping and deal conversion
Capability Building & Thought Leadership
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Develop:
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Reference architectures and reusable solution patterns
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Mentor:
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Engineers and consultants
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Contribute to:
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Internal capability development and AI maturity
Requirements
Consulting & Leadership
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7–12+ years in technology, data or solution architecture
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3–5+ years in consulting / client-facing architecture roles
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Proven experience:
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Leading AI or data engagements
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Managing multidisciplinary teams
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Engaging senior stakeholders and executives
AI & Generative AI
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Practical experience designing solutions involving:
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LLMs and Generative AI applications
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RAG architectures and retrieval systems
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AI agents / orchestration patterns
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Strong understanding of:
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Prompting, evaluation and guardrails
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Enterprise AI use cases and limitations
Solution Architecture
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Strong experience designing:
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Distributed systems and microservice architectures
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API-driven integrations
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Enterprise-scale cloud solutions
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Ability to clearly articulate architecture decisions and trade-offs
Cloud (At least one CSP, Azure preferred)
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Azure (preferred): OpenAI, AI Foundry, Synapse, Data Lake, App Services
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AWS: Bedrock, Lambda, S3
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GCP: Vertex AI, Cloud Run
Data & AI Platform Understanding
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Strong grounding in:
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Data engineering concepts (pipelines, modelling, lakehouse)
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AI system data flows (embeddings, chunking, indexing)
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Experience designing AI-ready data ecosystems
Business & Communication
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Ability to:
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Translate technical designs into business outcomes
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Communicate with C-suite and architecture boards
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Strong commercial acumen and delivery mindset
Please Note:
As all iqbusiness roles require honesty in the handling of or access to cash, finances, financial systems, or confidential information; our recruitment process requires that the following background checks be completed: credit, criminal, ID, and qualification verification
iqbusiness is committed to sustainable growth and transformation, we embrace diversity and employ previously disadvantaged individuals
