As AI Platform Lead, you will own both the architectural foundation for how AI is designed, governed and deployed, and the delivery and engineering leadership to make that architecture real. You will define the target AI architecture, reference patterns, standards and guardrails — and lead the squad(s) of AI Platform Engineers and Applied AI Engineers who build and operationalise them, taking solutions from concept to scaled production.
AI initiatives are already underway across business units but have grown organically with different tools and data pipelines. A core mandate is to work out the target AI architecture — positioning existing tools and platforms for what they are genuinely best suited to, introducing new capabilities only where a clear gap exists — while personally steering the technical direction and quality bar of the delivery teams. This is a hands-on leadership role: you will set direction and stay close enough to the build to review designs and code, unblock the squad, and grow the team's capability.
Key Responsibility:
Architecture Strategy & Design
- Define the Group's target AI architecture — serving front-, middle- and back-office operations across the value chain — and the roadmap to reach it from the current state.
- Assess existing technology landscape across core enterprise systems, data platforms and cloud infrastructure, and define where AI fits and how it integrates.
- Position existing tools and platforms for the use cases they are best suited to, and introduce new tools only where a clear gap exists — preventing duplication, fragmentation and avoidable cost.
- Design reference architectures for core AI patterns relevant (e.g. predictive and conversational analytics, computer vision for quality inspection and yield monitoring, document and contract intelligence, demand forecasting and supply chain optimisation).
- Produce a layered AI platform blueprint covering data ingestion, feature engineering, model development, deployment, monitoring and feedback loops.
Data, Automation, Integration & Infrastructure Foundations
- Work with the Data, Automation, Integration and Infrastructure Team Leads so their platforms come together as a coherent, AI-ready foundation — trusted data, feature stores, knowledge bases, embeddings and vector stores for ML, RAG and agentic use cases; coherent AI + automation integration; APIs/events exposing enterprise systems as a single trusted source; and scalable, secure, cost-effective compute/hosting.
- Set the AI-facing requirements, reference patterns and standards each platform must meet, providing architectural direction and design review, while each Team Lead owns the build and run of their platform.
Delivery & Engineering Leadership
- Lead, plan and own delivery for the Applied AI squad(s) — managing internal engineers and external / vendor developers, allocating work across initiatives, and ensuring on-time, production-grade delivery.
- Own the delivery roadmap, capacity and prioritisation in partnership with the AI Product Owners, and report progress and risks to IT Management.
- Act as the technical escalation point and design authority — providing hands-on guidance and code / design reviews.
- Lead the squad to design and deliver applied AI solutions (LLM applications, RAG pipelines, intelligent automation, predictive models) from concept to production, in line with the enterprise AI architecture.
- Develop robust evaluation harnesses to measure model accuracy, latency, cost and safety; iterate based on results.
- Line-manage, mentor and develop the AI Platform Engineers and Applied AI Engineers; translate ambiguous business requirements into well-scoped technical solutions, clearly communicating trade-offs.
Standards, Guidelines & Guardrails
- Develop the Group's AI architecture decision framework — build vs buy vs fine-tune, which platforms to standardise on, and how to evaluate new tools.
- Author AI architecture patterns, reference implementations and a technology radar for adoption by all implementation teams.
- Define model lifecycle standards (versioning, evaluation, explainability, drift detection, retirement) and establish MLOps / LLMOps practices appropriate to company's maturity and scale.
- Set cross-cutting platform standards for prompt management, identity and access, audit and logging — and prevent shadow AI through approved-tooling guidance and usage visibility.
Technology Evaluation & Roadmap
- Continuously scan the AI landscape (foundation models, vector databases, ML platforms, edge AI, agentic frameworks) and curate what is relevant to business context.
- Maintain a living AI technology roadmap with prioritisation tied to business value; lead proof-of-concept evaluations before technologies enter the stack.
- Serve as the technical authority in AI investment discussions and vendor evaluations.
Governance & Risk
- Embed responsible-AI principles — fairness, transparency, privacy and security — into architectural and delivery decisions from the ground up.
- Work alongside Cybersecurity and Legal to embed security, data-protection, regulatory and contractual requirements, and to align on AI risk controls, use-case approvals and audit.
- Ensure compliance with data sovereignty and cross-border data transfer requirements relevant to geographies, and establish model risk management for high-stakes use cases.
- Design AI platforms with cost transparency, usage attribution and chargeback in mind, in partnership with Infrastructure, Security and IT Finance.
Qualifications:
- Bachelor's or Master's in Computer Science, Engineering, Data Science, or related field.
- 8+ years in enterprise software / data engineering or architecture, including 3+ years focused on AI/ML system design and deployment at scale, and 3+ years leading or coordinating a delivery team (including internal engineers and external / vendor developers).
- Hands-on experience designing and deploying ML systems in cloud environments (preference for AWS and / or Azure), with proven experience productionizing ML/AI — API development, containerization, and CI/CD.
- Deep expertise building end-to-end LLM applications — agentic workflows, RAG systems and advanced prompt engineering — with strong programming proficiency (Python preferred) across GenAI frameworks (e.g. LangChain, LlamaIndex or equivalent).
- Strong command of the modern data and AI stack: data lakes / lakehouses, feature stores, vector databases, orchestration (e.g. Airflow, Prefect) and model serving infrastructure.
- Strong understanding of enterprise data architecture and a track record of partnering with data, integration and platform teams to make data AI-ready.
- Experience with both classical ML and modern generative AI / LLM architectures; practical understanding of fine-tuning, RAG and agentic patterns.
- Hands-on experience with AI-assisted development tools (GitHub Copilot, Claude Code, Cursor, or similar) and a track record of helping teams adopt them.
- Demonstrated ability to translate complex technical architecture into clear guidance for non-technical stakeholders; strong written communication (architecture documents, decision records, technology radars).
- Working knowledge of cloud cost management and platform governance (cost transparency, usage attribution, chargeback).
