Lead AI Architect - Enterprise Transformation

About QAD:

Qualifications

• 10+ years across software, platform, cloud, data or enterprise architecture, including meaningful experience architecting production AI/ML or GenAI systems — systems with real users, real failure modes and real operating cost, not pilots.
• Deep understanding of modern AI and agentic architectures: LLM platforms and model selection, agent orchestration, tool and API invocation, RAG and context architecture (chunking, hybrid retrieval, reranking, grounding, freshness), semantic layers, state and memory, evaluation and observability.
• Strong experience with at least one major cloud ecosystem — AWS and/or GCP — with the ability to evaluate services across platforms objectively rather than defending a favourite.
• Strong foundation in distributed systems and enterprise system engineering: APIs, integration patterns, authentication and authorisation, data architecture, application architecture, DevOps and CI/CD, and production operations.
• Experience designing architectures that span multiple enterprise applications and heterogeneous data sources, including systems you do not control.
• Ability to reason rigorously through build versus buy, reuse versus new capability, cost versus performance, and MVP versus target state — and to explain the reasoning to people who will be affected by it.
• Experience taking ambiguous business requirements through technical discovery into implementable designs.
• Non-functional design judgement: latency, availability, cost, failure modes, graceful degradation, and what happens when the model provider has an outage.
• Comfortable operating cross-functionally across engineering teams, vendors and stakeholders to hold alignment across design and delivery.
• Strong communication, with the ability to make complex architecture choices understandable to both technical and business leaders — and to say no to a bad idea without losing the relationship.
• Hands-on technical depth: able to inspect designs, APIs, data structures, prototypes and code, and credibly guide engineers.
• Enterprise SaaS products, multi-tenant platforms, or customer-facing AI applications.
• ERP, manufacturing, supply chain or industrial software domain exposure.
• Semantic and metadata platforms; building shared AI capability consumed by multiple products or business functions.
• Experience delivering through system integrators and cloud partners while retaining architectural control.
• Having taken an organisation through the second wave — the transition from a successful pilot to a repeatable production pattern, which is where most enterprise AI programmes stall.
• Familiarity with emerging interoperability standards for tools and agents (for example MCP and equivalent protocols), and a considered view of what to adopt and what to wait out.
• Experience in a security-sensitive or regulated environment.
• Not an advisory or strategy role. You will produce designs that engineers build from, not decks that recommend that someone else produce them.
• Not a people-management-first role. Leadership here is technical authority earned through design quality and judgement.
• Not a role that delegates the thinking to a partner. SIs will provide capacity; the architecture stays in-house.
• Not a research role. The bar is production systems, operating cost and reliability — not novelty.
• A clear enterprise AI reference architecture and MVP architecture, with documented design principles and decision guardrails.
• Priority AI workflows converted into implementable system designs, enabling engineering to move from PRD into build without a translation gap.
• The foundational capabilities required to support the first production AI workflows delivered, alongside a repeatable architecture for subsequent waves.
• Common patterns established for integration, identity and access, data and semantic services, orchestration, observability, evaluation and operations.
• A sustainable model for operating, monitoring, maintaining and evolving AI applications after launch.
• Recognition as the internal technical authority for the AI transformation, with internal engineers and external partners building against one coherent architecture.
• A golden path that measurably shortens the build time and cost of each successive workflow, with evidence from at least two subsequent waves.
• Known and improving unit economics — the business can see what each workflow costs to run, and that number is trending in the right direction.
• Architecture conformance functioning as a real gate, with partner-delivered work reviewed against the standard.
• No critical production incident attributable to a foreseeable architectural gap in identity, data access, evaluation or operational readiness.
• A credible, sequenced view of the next twelve months of platform evolution, endorsed by enterprise architecture and security.
• Portfolio conversation — walk us through an AI or platform architecture you owned end to end, including what you got wrong.
• Working session — a live architecture discussion on one of our actual prioritised workflows. We are interested in how you interrogate the problem, not in a polished answer.
• Depth probe — detailed technical examination of one system you designed, down to data flow, identity model and failure handling.
• Stakeholder interview — how you communicate trade-offs and hold a position with senior non-technical leaders.