Trust Architects build trust in the AI and trust among the people who use it. They make sure every system Avanade Next ships is safe, reliable and ready for production — and that the client's people are confident, capable and willing to adopt new ways of working alongside it.

The role operates across two dimensions. Trust in the AI: evaluation frameworks, accuracy measures, security controls, governance controls and human-in-the-loop safeguards. Trust in the people: adoption design, capability uplift, ways of working and confidence in human-agent collaboration.

Governance & Guardrails

• Set governance, risk and security guardrails before build begins.

• Embed controls, human review and feedback loops as the system evolves.

• Support regulatory and privacy positions with the right client and Avanade specialists.

Evaluation & Release Assurance

• Design and run evaluations, including accuracy targets, test harnesses and release evidence.

• Hold the release gate with delivery and quality teams.

• Work shoulder to shoulder with FDEs so trust and monitoring are built in.

Adoption & Ways of Working

• Design adoption and change approaches that build confidence through transparency, education and demonstrated outcomes.

• Help clients redesign human-agent interaction so systems fit real work.

• Track active usage and effective human oversight after go-live.

Contribution & Reuse

• Support executive and Board confidence with clear governance and accountability frameworks.

• Contribute evaluation harnesses, guardrail patterns, governance frameworks and adoption assets.

• Provide independent challenge where trust risks are not being addressed.

• Governance, guardrails and security controls in place before build starts.

• Target accuracy, safety and governance thresholds met before production.

• Adoption above agreed threshold at 90 days post-go-live.

• Compliance with responsible AI standards and client obligations.

• Reusable trust, evaluation and adoption assets contributed from every engagement.

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• 5+ years across AI governance, model risk, security, responsible AI, or large-scale technology change and adoption. Strength in two of these with a credible path into the third is expected — few candidates will have all of it.

• Hands-on evaluation experience with generative or agentic systems is strongly preferred over policy-only backgrounds.

• Working knowledge of the APAC regulatory environment relevant to their market.

• Practical change and enablement capability with technical audiences and frontline users alike.

• Able to build trust with sceptical stakeholders and to say no with evidence.

• Comfortable in a role that is still being defined, in a track with no established internal precedent.

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