[Hiring] Lead AI Architect @QAD, Inc.

Role Description

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.

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.
  • 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.
  • Define what is reusable enterprise capability versus workflow-specific build, and where capability should be centralised versus federated.
  • Lead platform and technology selection, evaluating options on functional fit, existing enterprise capability, build and migration effort, total cost of ownership, operating complexity, security, regulatory requirements and vendor lock-in.
  • Continuously evolve the architecture as new use cases, products and capabilities emerge.
• Delivery and production readiness
  • Drive the foundational platform build in parallel with workflow delivery.
  • Establish production architecture and engineering standards covering development, test and production environments.
  • Own the technical approach to AI operations — model and agent lifecycle management, evaluation, drift, reliability, observability, and cost and usage management.
  • Design the human-in-the-loop and escalation model at the architecture level.
  • Own the cost architecture, establishing unit economics per workflow.
• Data, identity and integration
  • Architect the data and integration foundations required by AI workflows.
  • Solve identity, delegated authority and traceability for agents.
  • 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.
  • Provide technical leadership to the AI engineering team and implementation partners.
  • Run architecture conformance review for partner-delivered work.

Qualifications

• 10+ years across software, platform, cloud, data or enterprise architecture, including meaningful experience architecting production AI/ML or GenAI systems.
• Deep understanding of modern AI and agentic architectures.
• Strong experience with at least one major cloud ecosystem — AWS and/or GCP.
• Strong foundation in distributed systems and enterprise system engineering.
• Experience designing architectures that span multiple enterprise applications and heterogeneous data sources.
• Ability to reason rigorously through build versus buy, reuse versus new capability, cost versus performance, and MVP versus target state.
• Experience taking ambiguous business requirements through technical discovery into implementable designs.
• Non-functional design judgement.
• Comfortable operating cross-functionally across engineering teams, vendors and stakeholders.
• Strong communication skills.
• Hands-on technical depth.

Requirements

• Strongly preferred experience in enterprise SaaS products, multi-tenant platforms, or customer-facing AI applications.
• Exposure to ERP, manufacturing, supply chain or industrial software domain.
• Experience delivering through system integrators and cloud partners while retaining architectural control.
• Familiarity with emerging interoperability standards for tools and agents.
• Experience in a security-sensitive or regulated environment.

Benefits

• Your health and well being are important to us at QAD.
• 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.
• An atmosphere of growth and opportunity, where idea-sharing is prioritized.
• Compensation packages based on experience and desired skill set.