Job Description
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:
Solution architecture (end-to-end systems design)
AI engineering (capability awareness, not hands-on build ownership)
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:
Senior member of the AI & Data capability working across multiple client engagements
Acts as the bridge between AI engineering, architecture, and business stakeholders
Owns solution design, architecture governance and delivery oversight
Plays a key role in pre-sales, client shaping, and capability development
Responsibilities:
AI Solution Architecture & Design
Lead the design of end-to-end AI architectures across data, application and integration layers
Design solutions spanning:
Generative AI (LLMs, RAG, agents)
Document intelligence and automation
Enterprise AI platforms and APIs
Define:
Data flow, integration patterns, and system architecture
Retrieval, orchestration and agent interaction patterns
Security, governance and deployment architectures
Client Advisory & Solution Shaping
Lead discovery workshops and use case definition sessions
Translate business problems into AI-enabled solutions and architecture blueprints
Advise clients on:
AI adoption roadmaps
Architecture approaches (build vs buy vs hybrid)
Trade-offs, risks, and ROI
Delivery Leadership
Own architecture across delivery lifecycle:
Discovery → design → build oversight → deployment → optimisation
Guide engineering teams on:
Architecture decisions
Design patterns and best practice
Ensure:
Production-grade delivery
Alignment to enterprise systems and constraints
Cloud AI Architecture
Architect solutions across at least one hyperscaler (Azure preferred), including:
Azure OpenAI, AI Foundry, AI Search, Document Intelligence
Equivalent AWS (Bedrock) or GCP (Vertex AI) services
Define:
Deployment patterns (APIs, microservices, serverless)
Integration into enterprise ecosystems
Security, networking and governance models
Data & AI Platform Design
Design data foundations required for AI:
Data pipelines, ingestion patterns, storage and modelling
Vector databases, embeddings and retrieval strategies
Ensure:
Data quality, lineage, and governance alignment
AI-readiness of enterprise data platforms
Pre-Sales & Commercial Contribution
Support and lead:
Solution design for proposals and RFPs
Estimation, costing and effort modelling
Contribute to:
Client pitches and demos
Opportunity shaping and deal conversion
Capability Building & Thought Leadership
Develop:
Reference architectures and reusable solution patterns
Mentor:
Engineers and consultants
Contribute to:
Internal capability development and AI maturity
Requirements
Consulting & Leadership
7 - 12+ years in technology, data or solution architecture
3 - 5+ years in consulting / client-facing architecture roles
Proven experience:
Leading AI or data engagements
Managing multidisciplinary teams
Engaging senior stakeholders and executives
AI & Generative AI
Practical experience designing solutions involving:
LLMs and Generative AI applications
RAG architectures and retrieval systems
AI agents / orchestration patterns
Strong understanding of:
Prompting, evaluation and guardrails
Enterprise AI use cases and limitations
Solution Architecture
Strong experience designing:
Distributed systems and microservice architectures
API-driven integrations
Enterprise-scale cloud solutions
Ability to clearly articulate architecture decisions and trade-offs
Cloud (At least one CSP, Azure preferred)
Azure (preferred): OpenAI, AI Foundry, Synapse, Data Lake, App Services
AWS: Bedrock, Lambda, S3
GCP: Vertex AI, Cloud Run
Data & AI Platform Understanding
Strong grounding in:
Data engineering concepts (pipelines, modelling, lakehouse)
AI system data flows (embeddings, chunking, indexing)
Experience designing AI-ready data ecosystems
Business & Communication
Ability to:
Translate technical designs into business outcomes
Communicate with C-suite and architecture boards
Strong commercial acumen and delivery mindset
Closing Date 30 September 2026