Senior Solution Architect(Data and AI)

We are seeking a senior Solution Architect – Data & AI to lead solutioning across both presales and delivery.

This role owns the end-to-end architecture for enterprise Data & AI solutions, from client engagement, proposal development and solution design through to delivery governance and implementation assurance. The ideal candidate combines deep technical expertise, commercial awareness and strong delivery experience.

Key Responsibilities

Presales & Client Solutioning

- Lead Data & AI solution architecture for RFPs, RFIs, tenders, proposals and client presentations.

- Engage Clients to understand business goals, technical needs, data maturity and AI opportunities.

- Translate requirements into solution architecture, scope, assumptions, risks, dependencies and delivery approach.

- Define high-level estimates, implementation roadmap and solution options.

- Work with sales, account teams, delivery teams and Partners to shape practical and commercially viable proposals.

- Present solution architecture and value proposition to senior client stakeholders.

Data & AI Architecture

- Design enterprise Data & AI solutions across cloud, hybrid and on-premises environments.

- Architect modern data platforms, including data lakes, data warehouses, lakehouses, analytics platforms and streaming solutions.

- Define architecture across data, AI, integration, infrastructure, security, operations and governance layers.

- Recommend fit-for-purpose technologies and patterns based on scalability, performance, reliability, security and cost.

- Design data ingestion, integration, ETL/ELT, transformation, metadata, lineage and data quality frameworks.

AI, GenAI & Advanced Analytics

- Design AI/ML and GenAI solution architectures, including LLMs, RAG, embeddings, vector databases and enterprise knowledge search.

- Assess AI use cases for feasibility, value, complexity, risk and operational impact.

- Define data pipelines and platform capabilities for AI workloads.

- Establish MLOps, ModelOps, model monitoring and AI lifecycle management approaches.

- Consider GPU compute, AI infrastructure, latency, scalability, privacy, security and cost.

Data Governance, AI Governance & Security

- Define data governance across data ownership, stewardship, metadata, lineage, quality, privacy, access control and compliance.

- Define AI governance across transparency, explainability, accountability, responsible AI, model risk, monitoring and human oversight.

- Ensure solutions align with enterprise architecture, Cyber, regulatory and industry standards.

- Work with risk, compliance, Cyber and architecture teams to validate governance and security controls.

Delivery Architecture & Technical Leadership

- Act as the architecture lead during delivery to ensure implementation aligns with approved solution design.

- Guide data engineers, AI engineers, platform teams and delivery teams through detailed design and build.

- Conduct architecture reviews, design validation, risk assessments and performance optimisation.

- Resolve technical trade-offs and delivery issues.

- Produce clear architecture documentation, diagrams, solution blueprints and technical specifications.

- Ensure smooth transition from presales to delivery with clear scope and ownership.

Qualifications

- Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, AI, Software Engineering or a related field.

- Minimum 15 years of technology experience, with strong experience in solution architecture, data architecture, AI architecture, enterprise architecture or large-scale platform delivery.

- Proven experience leading both presales solutioning and delivery architecture for enterprise Data & AI projects.

- Strong background in modern data platforms, cloud architecture, analytics, AI/ML, GenAI, integration, data governance, AI governance and security.

- Hands-on understanding of data lake, data warehouse, lakehouse, ETL/ELT, streaming, metadata, lineage and data quality patterns.

- Experience with cloud and data platforms such as AWS, Azure, Google Cloud, Databricks, Snowflake, Microsoft Fabric, Synapse, BigQuery or equivalent.

- Experience in consulting, technology services or large-scale enterprise transformation environments is preferred.

- Experience with public service, government or large enterprise Clients is advantageous.

- Strong client-facing communication, stakeholder management, documentation and presentation skills.

- Able to explain complex Data & AI topics clearly to both business and technical audiences.

- Commercially aware, delivery-focused, structured and comfortable managing ambiguity.

- Strong analytical and problem-solving ability, with the confidence to work across sales, architecture, engineering, Cyber and delivery teams.