QAD | Redzone is building an enterprise AI platform to support AI-enabled and agentic workflows across the organisation. The AI Centre of Excellence (CoE) is accountable for turning that ambition into deployed, measured, production workflows across priority business functions.

The CoE is small, senior and high-leverage. It combines architecture and engineering with a set of functional AI Engagement Specialists who own the demand side: what gets built, why it is worth building, whether anyone uses it, and whether it actually works once live.

We are looking for a Lead AI Architect to own the technical architecture underpinning this transformation — from translating business and product requirements into implementable system designs, to defining the reusable platform capabilities, technology choices and engineering standards required to build and operate AI solutions at scale.

This is a hands-on architecture leadership role. You will work across the AI CoE, enterprise architecture, engineering teams, business functions and implementation partners to ensure individual AI solutions are built on a coherent, scalable and maintainable foundation rather than as disconnected point solutions.

The distinguishing challenge here is not building one impressive AI workflow. It is building the second, fifth and twentieth at a fraction of the cost of the first, on infrastructure that survives contact with real enterprise identity, real permissions, real data quality and real production incidents.

Where this role sits

Reports to: Head of the AI Centre of Excellence

Alignment: Dotted line to QAD engineering architecture; close partnership with Enterprise Architecture, Security & GRC, Data & Platform Engineering, and Product

Technical leadership of: The CoE engineering team (Senior AI Engineer and subsequent hires) and the engineering capacity provided by system integrators and cloud partners

Primary internal customers: The functional AI Engagement Specialists who own the workflow pipeline and PRDs for the business functions in scope

Team size: No direct reports at hire; technical authority across an internal and partner engineering group. Line management may follow as the CoE scales.

What you will own

Architecture and platform direction

• Own the enterprise AI reference architecture, defining the target-state architecture and the MVP or “golden path” required to begin delivering priority AI workflows while the broader platform evolves.

• Translate workflow requirements and PRDs into build-ready technical architecture — system boundaries, source systems, APIs and integrations, data requirements, identity and access, user and consumption layers, orchestration, and non-functional requirements. Closing the gap between a high-level PRD and a design detailed enough to generate development stories is an explicit, named responsibility of this role.

• Make and govern key architecture decisions across infrastructure, data, integration, semantic layer, AI and LLM services, agent orchestration, state and memory, application layers and deployment architecture — and record them, with rationale and revisit triggers, as durable decision records.

• Define what is reusable enterprise capability versus workflow-specific build, and where capability should be centralised versus federated, balancing scalability against speed to value.

• Lead platform and technology selection, evaluating options on functional fit, existing enterprise capability, build and migration effort, total cost of ownership, operating complexity, the skills required to sustain the platform, security, regulatory requirements and vendor lock-in.

• Draw the boundary between internal enterprise workflow and productisable capability. In a software company, some of what the CoE builds will be a candidate for the product. Design so that boundary remains crossable rather than discovering later that an internal tool cannot be productised without a rewrite.

• Continuously evolve the architecture as new use cases, products and capabilities emerge — avoiding premature complexity while ensuring near-term decisions do not constrain the longer-term platform.

Delivery and production readiness

• Drive the foundational platform build in parallel with workflow delivery, identifying the minimum non-negotiable capabilities required before solutions can safely reach production rather than waiting for the complete target platform.

• Establish production architecture and engineering standards covering development, test and production environments, CI/CD, logging, monitoring, AI and application observability, tracing, state and memory, human–agent handoffs, and operational support.

• Own the technical approach to AI operations — model and agent lifecycle management, evaluation, drift, reliability, observability, and cost and usage management.

• Own evaluation as an architectural concern. Define how quality and correctness are specified, measured, regression-tested and monitored, and make evaluation infrastructure a first-class platform capability rather than something each workflow reinvents.

• Design the human-in-the-loop and escalation model at the architecture level: where a human must approve, where an agent may act autonomously, how handoffs preserve context, and how reversal or remediation works when an agent gets it wrong.

• Own the cost architecture. Establish unit economics per workflow, model routing and tiering, caching strategy, token budgets, and the telemetry required for the business to see what each workflow costs to run.

Data, identity and integration

• Architect the data and integration foundations required by AI workflows — APIs, semantic and translation layers, metadata, data access patterns, and appropriate reuse of existing enterprise platforms.

• Solve identity, delegated authority and traceability for agents. Design how an agent acts on behalf of a user across multiple systems under least privilege, how roles and permissions propagate into retrieval and tool invocation, and how every action is attributable to a human or an agent in an auditable trail. This is among the hardest problems in the architecture and is squarely owned by this role.

• Design for multi-tenancy and data isolation where AI capability touches customer data or customer-facing surfaces.

Governance and technical leadership

• Set architecture guardrails for security and responsible AI, partnering with existing Security, GRC and enterprise architecture functions rather than recreating them inside the AI team.

• Provide technical leadership to the AI engineering team and implementation partners — reviewing designs, resolving trade-offs, and ensuring builds conform to the reference architecture. You remain the internal technical authority even where significant engineering capacity is supplied by SIs or cloud partners.

• Run architecture conformance review for partner-delivered work, with the standing and the willingness to reject a design that will not survive production.

What we are looking for

Essential

• 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.

Strongly preferred

• 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.

The technical environment

You will work in a predominantly AWS estate with a meaningful GCP footprint, alongside multi-tenant SaaS products, ERP and manufacturing data, and a heterogeneous set of enterprise systems across CRM, support, services delivery, marketing and data platforms. Engineering capacity is a mix of internal team and external implementation partners.

We are deliberately not prescribing a fixed AI toolchain in this job description. Selecting it — defensibly, with the operating model and skills required to sustain it — is part of the mandate.

What this role is not

• 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.

What success looks like

First 3–6 months

• 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.

First 12 months

• 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.

How we assess

• 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.

Qualification

• Your health and well being are important to us at QAD. We provide programs that help you strike a healthy work-life balance.
• Opportunity to join a growing business, launching into its next phase of expansion and transformation.
• Collaborative culture of smart and hard-working people who support one another to get the job done.
• An atmosphere of growth and opportunity, where idea-sharing is always prioritized over level or hierarchy.
• Compensation packages based on experience and desired skill set

About QAD:

QAD | Redzone is redefining manufacturing and supply chains through its intelligent, adaptive platform that connects people, processes, and data into a single System of Action. With three core pillars — Redzone (frontline empowerment), Adaptive Applications (the intelligent backbone), and Champion AI (Agentic AI for manufacturing) — QAD | Redzone helps manufacturers operate with Champion Pace, achieving measurable productivity, resilience, and growth in just 90 days.

QAD is committed to ensuring that every employee feels they work in an environment that values their contributions, respects their unique perspectives and provides opportunities for growth regardless of background. QAD’s DEI program is driving higher levels of diversity, equity and inclusion so that employees can bring their whole self to work.

We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class.