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セールスエンジニア・プリセールスの求人666件

Asia AI Token Ops Solution Architect

給与
記載なし
勤務地
記載なし
休日
記載なし
雇用形態
記載なし

どんな仕事かAIによる生成

Shape Asia customer opportunities into clear TokenOps engagements, and lead workshops, architecture decisions, pilots, and prototypes across countries and time zones. Design Azure AI Foundry architectures and translate telemetry and architecture choices into FinOps unit economics and executive-ready value and scale decisions.

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この求人に応募

給与記載なし

募集元で応募する日本マイクロソフト株式会社の採用ページへ
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応募資格

必須

  • Bachelor's degree in a technical field, or equivalent experience, and 8+ years in customer-facing cloud or AI solution architecture.
  • Delivery leadership across multiple Asian markets and time zones, including customer workshops, pilots, architecture decisions, and production roadmaps.
  • Hands-on knowledge of Azure AI Foundry and production architecture for AI, agent, and RAG solutions.
  • Experience designing model routing and evaluation strategies across differing language, quality, latency, availability, and cost requirements.
  • Understanding of TokenOps observability and the telemetry needed to manage agent, model, token, cost, reliability, and outcome behavior.
  • Ability to model and explain FinOps unit economics and connect technical tradeoffs to customer value and scale decisions.
  • Strong data, API, identity, network, security, and enterprise-integration architecture skills.
  • Experience applying Responsible AI and enterprise governance requirements throughout architecture and delivery.
  • Ability to communicate with executive and engineering stakeholders and lead ambiguous decisions across cultures, organizations, and time zones.

歓迎

  • Azure Solutions Architect certification or equivalent demonstrated Azure architecture expertise.
  • Multilingual capability or substantial experience leading delivery in multilingual customer environments.
  • Knowledge of data, privacy, sovereignty, residency, and AI regulation across Asian markets.
  • Experience adapting global architecture patterns to local platforms, regulations, languages, and operating models.
  • Executive value-framing skills and experience connecting AI architecture to investment, economics, and business outcomes.
  • Consulting experience shaping scopes, work packages, effort, dependencies, risks, and success criteria.
  • Experience turning prototypes and regional field learning into reusable architecture and delivery patterns.
  • Ability to support engagement planning and controller implementation while collaborating with engineering, science, data, partner, and business-value teams.

募集要項最終確認 10/29

職種
セールスエンジニア・プリセールス
雇用形態
記載なし
給与
記載なし
勤務地
記載なし
働き方
記載なし
経験年数
8年以上
学歴
大学卒業以上
掲載日
2026/10/28 14:26(1日前)
最終確認
2026/10/29 02:26(今日)

仕事内容

募集内容

  • Shape Asia opportunities by connecting customer priorities, market context, AI use cases, and operating constraints to a clear TokenOps engagement.
  • Lead customer workshops and architecture decisions across countries, cultures, languages, and time zones.
  • Design Azure AI Foundry architectures for agents, RAG, model access, orchestration, grounding, and enterprise integration.
  • Lead pilots and prototypes that validate feasibility, quality, performance, cost, security, and adoption in the target market.
  • Define model routing and evaluation approaches that account for language, locality, quality, latency, availability, and cost.
  • Design TokenOps observability across applications, agents, models, gateways, tokens, costs, controls, and customer outcomes.
  • Translate telemetry and architecture choices into FinOps unit economics and executive-ready value and scale decisions.
  • Guide data, API, identity, network, platform, and legacy-integration decisions across diverse customer estates.
  • Embed enterprise security and Responsible AI requirements while addressing market-specific data, sovereignty, privacy, and AI regulation.
  • Create reusable Asia shaping and delivery patterns and bring regional requirements into the broader TokenOps product and practice.
  • Asia opportunities have a clear customer outcome, architecture, work package, regulatory approach, and path from pilot to production.
  • Workshops and pilots produce decisions that account for language, locality, market constraints, unit economics, and operational readiness.
  • Customer architectures integrate agents, RAG, routing, evaluations, observability, data, APIs, security, and Responsible AI coherently.
  • Distributed stakeholders can make and execute architecture decisions effectively across markets, organizations, and time zones.
  • Regional delivery patterns are reusable while remaining adaptable to country-specific data, sovereignty, and AI requirements.

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