Principal AI Architect

Role Overview:
Leading a team of comprising of Architects and Engineers, we are seeking a Principal AI Architect who is technically strong and hands-on to help design AI systems and build models and proofs of concept. From this, you will also have the confidence in your solutions to be able to explain and defend these designs to executives, clients and evaluation panels alike.

Reporting the GM of AI, the role will exercise functional leadership over the AI architectural practice to build on the established foundations and help build and grow the practice for ongoing success.



The 4 Key Responsibilities for this role are:

AI Consulting (AI Architecture Lead)

• Lead AI architecture strategy and design, from technical readiness and use-case assessment through to target-state architecture, platform and hosting decisions.
• Define and sequence the AI roadmap, including technology choices, technical dependencies, delivery effort and business case inputs.
• Embed Responsible AI by design, translating governance, privacy and compliance requirements into architecture controls.

AI Development

• Lead AI engineering delivery, setting technical direction, mentoring engineers, governing design decisions, and ensuring production-ready outcomes.
• Design and build AI solutions, including model selection and optimisation, GenAI applications, RAG, agents, integrations, and AI platform architecture.
• Establish AI engineering and operations practices, covering data pipelines, MLOps/LLMOps, evaluation, security, observability, CI/CD, and cost optimisation.
• Own AI architecture standards and quality, maintaining reference architectures, patterns, governance, and compliance across delivery.

Pre-Sales : Technical Solution Shaping

• Lead technical solutioning for opportunities, conducting discovery, designing AI solutions, and defining architecture, integrations, hosting and delivery approaches.
• Develop technical inputs for bids and business cases, including sizing, estimates, infrastructure requirements, risks, assumptions and technical scope.
• Support pursuits and client evaluations, authoring tender responses, delivering demos and defending solutions in presentations and evaluation panels.
• Enable successful delivery, maintaining reusable solution assets and ensuring clear handover of architecture, plans and acceptance criteria to delivery teams.

AI Technical Advisory : Service Transformation

• Advise on the business’ AI transformation, including architecture, technology choices and prioritisation of internal AI use cases.
• Design and assure internal AI solutions and platforms, covering AIOps, automation, agent assist, self-service, security, governance and cost optimisation.
• Lead AI validation and value realisation, through POCs, benefits measurement and conversion of proven capabilities into client-facing offerings.

Essential Skills and Experience

• 12+ years’ experience in software, solution and enterprise architecture, with 5+ years delivering AI, ML and GenAI solutions.
• Proven leadership of AI engineering teams, driving models and AI solutions from concept to production.
• Deep hands-on expertise in model development, fine-tuning, evaluation and proof-of-concept delivery.
• Strong consulting and presales capability, leading AI architecture engagements, solution design, bids and tender responses.
• Excellent stakeholder engagement and communication skills, with the ability to influence executives and translate complex technical concepts for non-technical audiences.

Technical Skills:

• Strong AI engineering expertise, with hands-on proficiency in Python, PyTorch and end-to-end AI solution development.
• Deep knowledge of GenAI and LLM architectures, including fine-tuning, RAG, agents, multi-agent systems, guardrails and model optimisation.
• Experience designing and operating AI platforms, covering model serving, GPUs, Kubernetes, open-source models and hybrid/sovereign cloud environments.
• Strong data and AI operations capability, including vector databases, data pipelines, MLOps/LLMOps, observability and model lifecycle management.
• Expertise in AI security and governance, including access control, data protection, prompt-injection defence and model supply-chain security

Qualifications

• Degree in computer science, engineering, mathematics or a related field
• Relevant certifications preferred, including hyperscaler AI or architecture certifications, TOGAF, and NVIDIA professional certifications in AI infrastructure, operations and generative AI.

This is a unique opportunity to help shape and define the organisation’s Architectural AI team and wider practice so it is ultimately best-of breed.



If you are interested in discovering more about this opportunity, please apply with your resume in MS Word format only (.doc or .docx).

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