アマゾンウェブサービスジャパン合同会社のSenior AI Specialist Solutions Architect, AWS求人
アマゾンウェブサービスジャパン合同会社
- 職種
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仕事内容
As AI Specialist Solutions Architects (SSAs) with Amazon Web Services (AWS), we play a pivotal role in advancing AI adoption by crafting scalable, flexible, and resilient technical architectures tailored to meet complex customer challenges. Our primary task involves nurturing robust technical relationships with customers, acting as their trusted advisor to ensure they maximize the efficacy of cloud technologies throughout their digital transformation journeys. We focus on enhancing projects involving Generative AI (GenAI), Machine Learning (ML), and Agentic technologies across various organizations.
Our responsibilities extend to managing the technical relationships, advising on matters of security, cost, performance, and reliability to expedite the deployment of complex GenAI/ML solutions. We serve as a conduit for customer feedback, influencing AWS roadmap developments by representing the customer’s voice regarding their service usage.
Creativity is key in our role as we convert AI ambitions into executable and scalable solutions in production environments. We have the opportunity to define cloud-native GenAI/ML architectures for diverse use cases, and participate in publishing best practices, creating technical content, and developing new reference architectures. To achieve this, we engage in producing whitepapers, code samples, blog posts, and also host educational workshops and user meetups to evangelize the AWS technology for running GenAI/ML workloads.
We also provide technical leadership and mentorship through conducting deep dives and technical workshops, contributing to reusable code and building technical assets to support the broader engineering community. This role requires profound experience in AI system design, the implementation of distributed applications, and a robust understanding of AI framework management, including the integration and fine-tuning of large-scale language models and other cutting-edge AI technologies. Additionally, hands-on experience with AWS' Bedrock, AgentCore, and SageMaker is highly valued, along with a strong background in cloud technology certification.
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 | - |
| リモートワーク | - |
| その他 | - **応募要件**: - 7+ years of in design/implementation/operations/consulting with distributed applications experience - 4+ years of management of technical, enterprise customer facing resources or equivalent experience - 5+ years of design/implementation of production AI systems. - Experience implementing AI solutions that can include integration of LLMs/multi-modal FMs in large scale systems, fine-tuning LLMs, deployment and distributed inference of LLMs, RAG, FM evaluation, Vector DBs, Agentic workflows, prompt/context engineering, and MLOps. - **望ましい経験**: - Hands-on experience with AWS ecosystems (including Bedrock, AgentCore, and SageMaker) to set up secure, private-network AI environments, and practical experience implementing Retrieval-Augmented Generation using embeddings, vector stores, and semantic search optimization. - Able to effectively communicate across an increasing diversity of audiences internally and externally - Ability to influence customer and internal business decision makers as a technical thought leader - Cloud Technology Certification (such as Solutions Architecture, Cloud Security Professional or Cloud DevOps Engineering) - Proven ability to lead projects with complex challenges with extensible, operationally excellent, cost optimized, and aligned solutions outcomes - Ability to lead a team or small organization-wide initiative with business objectives that are partially defined - Strong ability to determine solution strategy and where to simplify or extend solutions for the best outcome - Master's degree in computer science, mathematics, statistics, machine learning or equivalent quantitative field, or PhD Experience in running & fine-tuning Large and Small Language Models using advanced techniques like LoRA/QLoRA, Instruction Tuning, and RLHF to optimize for specific domain tasks. |
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