Role Context & Reporting Line:
- Reports to the AI Capability Lead (Data & Analytics).
- Works as part of a multidisciplinary AI delivery team across multiple client business units.
- Engages senior stakeholders, SteerCo and (where appropriate) C-suite, Model Risk and Architecture Boards.
- Supports the build-out of the AI capability: partnerships with Microsoft, AWS, Google, Databricks and Anthropic; pre-sales support; PoC and production delivery on cloud AI solutions.
Key Responsibilities:
AI & Generative AI Engineering
- Design, build and deploy Generative AI and LLM-based applications, including end-to-end RAG agents and agentic / multi-agent solutions.
- Implement RAG pipelines: chunking strategies, embeddings, dynamic indexing, vector databases, vector indexing, grounding and evaluation.
- Build document intelligence solutions: OCR, classification, custom/neural extraction, table extraction and post-processing for unstructured data.
- Implement tool/function calling, prompt engineering, fine-tuning and guardrails for production AI agents.
- Integrate AI models into enterprise systems via APIs, Service Bus, web apps and downstream platforms.
- Experience/knowledge of fine-tuning generative AI models, MCP, AI tool calling, A2A and graph databases.
Cloud AI Solution Delivery Proficient in any of the following (At least 1 CSP) (Azure | AWS | GCP)
- Azure: Azure OpenAI, AI Foundry / Prompt Flow, AI Search, Cognitive Services, Document Intelligence, Functions, Container Apps, Web Apps, Synapse, Data Lake, DevOps CI/CD.
- AWS: Amazon Bedrock (Anthropic/Claude, Titan Embeddings), Lambda, S3 data lakes, Textract and supporting services for AI agents and RAG.
- GCP: Vertex AI, Cloud Run, Google AppSheet and supporting services for AI workloads.
- Microsoft Fabric & Power Platform: Copilot Studio, AI Builder, Power Apps, Power Automate for rapid AI / automation delivery.
- Databricks: notebooks, ML workflows, Lakehouse and Generative AI capabilities.
- Design and implement cloud AI architectures, including migration patterns across hyperscalers where required.
Data Engineering for AI (AI-Data Engineering)
- Design and implement reliable data pipelines (Python, SQL, PySpark) to support ML and AI workloads.
- Prepare, transform and manage structured and unstructured data for AI use cases (ingestion, ETL/ELT, modelling, lakehouse).
- Implement chunking, embedding, indexing and retrieval mechanisms across vector stores.
- Ensure data quality, lineage and governance alignment, including Purview / catalog tooling where applicable.
AIOps & Operationalisation
- Build CI/CD pipelines for ML and AI models (Azure DevOps, GitHub Actions or equivalent).
- Manage model deployment, monitoring, versioning and performance optimisation.
- Implement scalable, secure inference architectures (Container Apps, Lambda, Cloud Run, Functions).
- Apply Responsible AI, model risk, security and compliance practices (RBAC, Key Vault / Secrets Manager, VNets / Private Endpoints, Monitor / Log Analytics).
Consulting & Delivery
- Engage client stakeholders and translate business requirements into AI solution designs.
- Contribute to discovery, design, estimation, costing and commercial models.
- Communicate risks, trade-offs, model assumptions and limitations clearly to technical and business audiences.
- Produce solution architecture, status reports, SteerCo material, governance artefacts and user documentation.
- Support pre-sales, demos, PoCs and RFP responses; contribute to the AI capability roadmap and uplift of junior engineers.
Required Skills & Experience:
- Degree in Computer Science, Data Science, Engineering, Mathematics or a related quantitative field.
- 3+ years' experience delivering AI / ML / data solutions, ideally in a consulting or enterprise delivery environment.
- 1–2+ years' hands-on Generative AI engineering experience (LLMs, RAG, embeddings, vector DBs, prompt engineering).
- 3+ years' broader ML / AI delivery experience (supervised ML, feature engineering, evaluation, NLP).
- Strong data engineering: pipelines, Python / PySpark, data modelling, lakehouse patterns.
- Cloud experience on at least one of Azure, AWS or GCP, with working knowledge of a second; containerisation and CI/CD.
- Experience integrating AI into enterprise systems via APIs, web apps and messaging.
- Business acumen: ability to link AI solutions to business value, ROI and risk.
- Strong communication, stakeholder management, collaboration and analytical skills.
Advantageous Certifications in Any of the following:
Certifications – AWS (AI / ML & Architecture)
- AWS Certified AI Practitioner.
- AWS Certified Machine Learning – Specialty.
- AWS Certified Machine Learning Engineer – Associate.
- AWS Certified Solutions Architect (Associate or Professional).
- AWS Certified Data Engineer – Associate.
Certifications – Microsoft Azure (AI & Data)
- Microsoft Certified: Azure AI Engineer Associate (AI-102).
- Microsoft Certified: Azure AI Fundamentals (AI-900).
- Microsoft Certified: Azure Data Scientist Associate (DP-100).
- Microsoft Certified: Fabric Analytics Engineer Associate (DP-600) or Fabric Data Engineer Associate (DP-700).
- Microsoft Certified: Azure Data Engineer Associate (DP-203).
- Microsoft Certified: Azure Solutions Architect Expert (AZ-305).
- Microsoft Applied Skills credentials in Generative AI, Azure OpenAI, Semantic Kernel, Copilot, AI Builder or Document Intelligence.
Certifications – Google Cloud (AI & Data)
- Google Cloud Certified – Professional Machine Learning Engineer.
- Google Cloud Certified – Generative AI Leader.
- Google Cloud Certified – Professional Data Engineer.
- Google Cloud Certified – Professional Cloud Architect.
- Google Cloud Certified – Cloud Digital Leader.
Certifications – Other AI / Data Platforms
- Databricks Certified Generative AI Engineer Associate.
- Databricks Certified Machine Learning Associate / Professional.
- Databricks Lakehouse Fundamentals / Data Engineer.
- Anthropic / Claude developer credentials.
- NVIDIA Deep Learning Institute (DLI) certifications in Generative AI or LLMs.
- Harvard or other recognised Data Science / Machine Learning credentials.
Other Advantageous Experience
- Microsoft Fabric, Azure AI Foundry, Azure OpenAI and solution delivery experience.
- AWS Bedrock with Anthropic Claude, Titan Embeddings and Textract in production.
- GCP Vertex AI and Cloud Run delivery experience.
- Knowledge graphs, advanced RAG patterns, agent orchestration and multi-agent frameworks.
- Exposure to Model Risk Management (MRM), Architecture Review Boards and Responsible AI frameworks.
- Experience productising AI solutions and contributing to AI CoE / Target Operating Model design.
- Track record in pre-sales, RFPs, technical demos and client workshops.
Success Measures:
- Production-grade AI solutions deployed across Azure, AWS and / or GCP.
- Scalable, governed data and AI pipelines established and reused across engagements.
- Measurable contribution to revenue, pre-sales and RFP wins.
- Reduced time-to-production for new AI use cases through reusable patterns and accelerators.
- Demonstrable mentorship of junior engineers and uplift of the broader AI capability.
- High-quality stakeholder engagement, SteerCo and executive communication.
Closing Date 30 September 2026
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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
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