Lead AI Architect

About the role

We are looking for a Lead AI Architect to lead the design and architecture of AI products, from problem definition and technical discovery through production delivery and continuous improvement. You will turn business and product goals into secure, scalable, measurable AI solutions and help teams choose the right approach across a fast-changing AI landscape.

You will work closely with product owners, enterprise architects, engineering, data, security and operations teams. This is a hands-on technical leadership role: you will own solution architecture, validate critical design choices through prototypes and reference implementations, and guide teams through delivery.

What you will do

• Partner with product owners to define AI use cases, user journeys, feasibility, business outcomes and acceptance criteria. Challenge when conventional software or analytics is a better fit than AI.
• Lead end-to-end AI product architecture across experience, application, model, data, integration and infrastructure layers. Document decisions, trade-offs, dependencies and non-functional requirements.
• Design appropriate solutions using predictive ML, generative AI, retrieval-augmented generation (RAG), multimodal models and agentic workflows. Use autonomous or multi-agent designs only where they add value.
• Work with enterprise architects to align solutions with target architectures, integration patterns, platform standards and governance. Build reusable reference architectures and components without duplicating enterprise capabilities.
• Evaluate models, platforms, frameworks and vendors through structured experiments. Recommend build-versus-buy decisions based on quality, security, latency, total cost, portability and operating needs.
• Guide engineering teams through implementation, architecture and code reviews, integration and production readiness. Prototype high-risk assumptions and mentor engineers and other architects.
• Embed responsible AI and security by design: privacy, access controls, permission-aware retrieval, tenant isolation, prompt-injection defenses, safe tool use, audit trails and human approval for high-impact actions.
• Define evaluation, testing, monitoring and lifecycle controls for models, prompts, retrieval and agents. Plan fallbacks, failure handling, rollbacks and incident ownership with platform and operations teams.
• Track the AI landscape and translate developments into practical roadmaps and guidance. Communicate decisions clearly to technical teams, product owners and senior stakeholders.