Enterprise AI & Data Solution Architect

The Enterprise AI & Data Solution Architect is responsible for designing credible, scalable and secure AI/Data solution architectures that support iqbusiness’ solution-led growth agenda. The role partners with Sales, Data / AI Solution Leads, Service Line Leaders and delivery teams to ensure that client solutions are technically sound, commercially realistic and aligned to enterprise architecture principles.

This role brings technical credibility into sales and solutioning conversations, helping clients understand how AI, Data, cloud platforms, governance, integration, analytics and modern architecture can be combined to deliver measurable business outcomes.

Role Mandate:

The role owns the architecture integrity of AI/Data-enabled solutions from early-stage pre-sales through proposal, mobilisation and design assurance. It is a pre-sales and solution architecture role with delivery oversight, not a day-to-day project management role.

Responsibilities:

Enterprise and Solution Architecture

• Design end-to-end AI/Data solution architectures across data platforms, integration, analytics, AI/ML, GenAI and enterprise technology landscapes.
• Translate client business priorities into practical architecture options and solution designs.
• Define target-state architectures, transition approaches and architecture roadmaps.
• Ensure solutions are scalable, secure, governed, cost-aware and fit for enterprise deployment.
• Identify architecture risks, technical dependencies and implementation constraints early in the sales cycle.

Pre-Sales Architecture and Solutioning

• Support early-stage client conversations where technical credibility is required.
• Work with Data / AI Solution Leads and Sales Executives to shape solution options, technical responses and proposal content.
• Contribute architecture diagrams, solution blueprints, technical assumptions, delivery estimates and platform recommendations.
• Participate in client workshops, solution presentations, orals and technical due diligence sessions.
• Help ensure that proposals are technically coherent and differentiated.

AI, Data and Platform Design

• Design architectures for AI-enabled data platforms, analytics solutions, GenAI solutions, enterprise search, document intelligence, AI agents, RAG architectures and integrated data products.
• Advise on data readiness, data modelling, ingestion, transformation, storage, governance and security requirements.
• Support MLOps, model deployment, monitoring, evaluation and operational governance considerations where relevant.
• Ensure responsible AI, privacy, data protection, model risk and human oversight principles are embedded into solution design.

Design Authority and Delivery Oversight

• Provide architecture oversight during mobilisation and early delivery for strategic or complex solutions.
• Support design checkpoints, architecture assurance reviews and escalation resolution.
• Work with delivery teams to protect the solution intent established during pre-sales.
• Provide guidance where architecture, integration, data quality, platform constraints or stakeholder alignment become delivery risks.

Reusable Assets and Capability Building

• Develop reference architectures, architecture patterns, technical templates and reusable accelerators.
• Contribute to the EMA AI/Data solution catalogue and architecture playbook.
• Coach internal teams on AI/Data architecture patterns, modern data platform principles and responsible AI design.
• Stay informed on emerging AI, cloud, data and enterprise architecture patterns and translate these into practical consulting assets.

Requirements:

Essential

• 10+ years' experience in Solution Architecture, Enterprise Architecture, Data Architecture or related technology leadership roles in consulting, professional services or complex enterprise environments.
• Strong architecture experience across modern data platforms, cloud, integration, analytics and AI-enabled solutions.
• Experience designing enterprise or client-facing AI/Data solutions from discovery through to implementation.
• Understanding of GenAI, LLMs, RAG, AI agents, vector databases, MLOps, data governance and model risk considerations.
• Ability to translate complex technical concepts into business-friendly language.
• Comfortable supporting pre-sales, proposal development, solution estimation and client presentations.
• Strong stakeholder engagement skills across business, technology, architecture, risk and delivery teams.

Advantageous

• Azure, AWS, Databricks, Snowflake, Synapse, Microsoft Fabric, Power BI or similar platform…