アマゾンウェブサービスジャパン合同会社のWorldwide Specialist Solutions Architect - GenAI, Data & AI GTM求人
アマゾンウェブサービスジャパン合同会社
- 職種
- ―
- 年収
- ―
- エリア
- 東京
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仕事内容
We are responsible for defining the future of Go to Market (GTM) at AWS using generative AI (GenAI). As part of the worldwide GenAI Training and Inference team, our role involves crafting and executing targeted strategies to boost customer adoption of AWS services across various industry verticals. We directly collaborate with key customers to help them scale and optimize large-scale workloads, such as foundation models, on AWS. Our tasks include model performance evaluations, developing demos, proof-of-concepts, and GTM plans, as well as engaging in external and internal evangelism.
We develop cloud-based solutions to advance the GenAI business, working closely with engineering teams to enable new capabilities for deploying GenAI workloads on AWS. Our responsibility includes aiding AWS technical community, solution architects, and sales teams with customer-centric value propositions and demonstrations of GenAI on AWS. We leverage our technical expertise and business acumen to engage with startups, enterprises, and AWS partners, articulating the potential and challenges of GenAI models across all organizational levels.
We are tasked with creating GTM plans for GenAI on AWS, comprehending customer business challenges, and crafting scalable GenAI solutions. This involves working with account teams, research scientists, and product teams to implement models and solutions. We are passionate about educating companies and partners on AWS best practices and have a natural inclination to innovate and apply new technologies. Additionally, we collaborate with AWS ML and EC2 product teams to influence the product vision and prioritize features for AI/ML applications. Our role extends to identifying industry patterns and trends that can accelerate innovation across customer segments.
募集要項
| 勤務地 | Location not specified. |
|---|---|
| 給与 | - |
| 勤務時間 | - |
| 休暇制度 | - |
| 待遇・福利厚生 | - Work/Life Balance (flexibility as part of working culture) - Inclusive Team Culture (employee-led affinity groups) - Mentorship and Career Growth (knowledge-sharing, mentorship, and career-advancing resources) Note: Other specific mentions of待遇・福利厚生 are not provided. |
| リモートワーク | - |
| その他 | - **応募要件**: - 8+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience - 3+ years of design, implementation, or consulting in applications and infrastructures experience - Experience developing, deploying and managing AI products at scale - Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience working with PyTorch or JAX software - Hands-on experience benchmarking and optimizing performance of models on accelerated computing (GPU, TPU, AI ASICs) clusters with high-speed networking. - Experience deploying and serving large language models for inference using container orchestration platforms like Kubernetes. - Hands-on understanding of deep learning and other ML algorithms and infrastructure. - Knowledge of MLOps tools and workflows for model development, validation, and deployment. - Experience working with field teams to drive adoption of ML solutions。 - **望ましい経験**: - Experience working with end user or developer communities - Master's degree or above in engineering or equivalent STEM (Science, Technology, Engineering and Mathematics) field - Experience with at least one general-purpose programming language such as Java, Python, C++, C#, Go, Rust, or TypeScript - Experience communicating clearly and concisely with leadership, stakeholders, and cross-functional teams - Experience working with end user or developer communities - Experience working with 3rd party AI model providers to evaluate model quality/performance. - Experience deploying models on model hosting platforms and/or working with early adopters of model APIs. - Knowledge of vertical use cases for large language models in industries like finance, healthcare, retail etc. - Demonstrated ability to work effectively across internal and external organizations. - Ability to influence product roadmaps based on customer needs and market traction. |
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