Data and AI Products Architect

Role Overview

This role owns the technical architecture of the Data & AI product portfolio and acts as the Technical counterpart to the Product Owner. Works with Innovation on PoC-to-product transitions and applies AI governance and Responsible AI standards. Responsible for architecting products for multi-tenancy and non-functional characteristics.

Key responsibilities

Product architecture & portfolio

• Own the target architecture for each product in the portfolio, designing for reuse, configurability, and multi-tenancy.

• Define the shared platform layer — common services, components, and infrastructure reusable across products — and drive consolidation of duplicated capability.

• Own architecture roadmap in sync with product roadmap.

AI & GenAI solution design

• Responsible for design decisions: build vs. buy, model selection, fine-tuning vs. retrieval, technical debt management and trade-offs.

• Design retrieval architectures (chunking, embedding, indexing, hybrid search, and re-ranking) for accuracy and latency at scale.

• Define the data architecture for AI solutions — training data, feature pipelines etc.

• Architect observability, monitoring, and drift detection

Product maturation and disposition

• Assess PoCs and early MVPs for technical viability, scalability, and productisation.

• Conduct product technical readiness assessments; provide the architectural basis for product disposition decisions.

Production readiness & unit economics

• Design the LLMOps/MLOps backbone for the portfolio — serving, versioning, deployment pipelines, and rollback.

• Define evaluation architecture: eval harnesses, quality benchmarks, regression testing.

• Own the cost model — inference economics and token cost — as an input to product pricing.

Safety, governance & technical leadership

• Embed Responsible AI, explainability, and AI security standards into product designs. Design for the regulatory obligations.

• Support the Product Owner with technical input to funding pitches, business cases, and customer conversations.

Experience

• 10–15 years in software/ML engineering and architecture, with proven AI solutions in production

Expected Skills

• GenAI architecture — LLMs, RAG, agentic patterns, orchestration frameworks

• MLOps/LLMOps, evaluation design, model serving, observability, and inference cost optimisation

• Experience in architecting commercial software products - Multi-tenancy, versioning, and productisation of prototypes

• Working knowledge of data platforms, pipelines, and modelling

• Ability to connect architectural choices to unit economics, profitability and time-to-market

• Guide the product development teams with implementation and standards compliance

Function: Information Technology
Experience Level: Mid-Senior Level