Deep Learning Architect, GenAI Innovation Center
6 개월전에
The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train and fine tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently.
You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.
We’re looking for top architects, system and software engineers capable of using ML, Generative AI and other techniques to design, evangelize, implement and fine-tune state-of-the-art solutions for never-before-solved problems.
머신 러닝과 AI의 최전선에서 일하고 싶으신가요?
최첨단 제너레이티브 AI 알고리즘을 적용하여 실제 문제를 큰 영향을 미치며 해결하게 되어 기대되시나요?AWS의 제너레이티브 AI 혁신 센터는 AWS 고객이 제너레이티브 AI 솔루션을 구현하고 혁신적인 비즈니스 기회를 실현할 수 있도록 지원하는 새로운 전략 팀입니다.전략가, 데이터 사이언티스트, 엔지니어 및 솔루션 아키텍트로 구성된 팀이 고객과 단계별로 협력하여 제너레이티브 AI의 힘을 활용하는 맞춤형 솔루션을 구축합니다.
이 팀은 고객이 비즈니스에 최고의 가치를 창출할 사용 사례를 구상 및 범위를 지정하고, 적합한 모델을 선택 및 교육 및 미세 조정하고, 기술 또는 비즈니스 과제를 탐색하기 위한 경로를 정의하고, 개념 증명을 개발하며, 대규모 솔루션 출시를 위한 계획을 세울 수 있도록 지원합니다. GenAI Innovation Center 팀은 제너레이티브 AI를 책임감 있고 비용 효율적으로 적용하는 모범 사례에 대한 지침을 제공합니다.
또한, 고객과 직접 협력하여 빠르게 변화하는 조직에서 혁신을 이루어 판도를 바꾸는 프로젝트 및 기술에 기여하게 될 것입니다.실험을 설계 및 실행하고, 새로운 알고리즘을 연구하고, 위험, 수익성 및 고객 경험을 최적화하는 새로운 방법을 찾게 됩니다.
ML, Generative AI 및 기타 기술을 사용하여 이전에 해결하지 못한 문제에 대한 최첨단 솔루션을 설계, 홍보, 구현 및 미세 조정할 수 있는 우수한 아키텍트, 시스템 및 소프트웨어 엔지니어를 찾고 있습니다.
Key job responsibilities
In this role, you will:
• Collaborate with our applied and data scientists to build robust and scalable Generative AI solutions for business problems
• Effectively use Foundation Models available on Amazon Bedrock and Amazon SageMaker to meet our customer's performance needs
• Work hands on to build scalable cloud environment for our customers to label data, build, train, tune and deploy their models
• Interact with customer directly to understand the business problem, help and aid them in implementation of their ML ecosystem
• Analyze and extract relevant information from large amounts of historical data to help automate and optimize key processes
• Work closely with account teams, applied/data scientist teams, and product engineering teams to drive model implementations and new algorithms
• Mentor and develop junior members on the team
이 역할은 다음의 업무를 수행하게 됩니다.
• 당사의 응용 및 데이터 사이언티스트와 협력하여 비즈니스 문제를 위한 강력하고 확장 가능한 제너레이티브 AI 솔루션을 구축합니다.
• Amazon Bedrock 및 Amazon SageMaker에서 사용할 수 있는 기반 모델을 효과적으로 사용하여 고객의 성능 요구 사항을 충족합니다.
• 직접 작업하여 고객이 데이터에 레이블을 지정하고, 모델을 구축, 교육, 조정 및 배포할 수 있는 확장 가능한 클라우드 환경을 구축합니다.
• 고객과 직접 상호 작용하여 비즈니스 문제를 이해하고 ML 에코시스템을 구현하는 데 도움을 주고 지원합니다.
• 대량의 과거 데이터에서 관련 정보를 분석하고 추출하여 주요 프로세스를 자동화하고 최적화하는 데 도움을 줍니다.
• 어카운트 팀, 응용/데이터 사이언티스트 팀, 제품 엔지니어링 팀과 긴밀하게 협력하여 모델 구현과 새로운 알고리즘을 추진합니다.
• 팀의 주니어 구성원을 멘토링합니다.
About the team
Sales, Marketing and Global Services (SMGS)
AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest-growing small- and mid-market accounts to enterprise-level customers, including the public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. The Professional Services team is part of Global Services.
About AWS
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
#aws-korea-proserv-ap
#AWSKOREA
We are open to hiring candidates to work out of one of the following locations:
Seoul, KOR
BASIC QUALIFICATIONS
- Bachelor's degree in computer science or equivalent with 5+ years of relevant working experience- Experience with machine learning fundamentals, with working knowledge of Python and experience with deep learning frameworks such as Pytorch, TensorFlow, JAX or MXNet
- 5+ years of relevant experience in developing and deploying large scale machine learning or deep learning models and/or systems into production, including batch and real-time data processing
PREFERRED QUALIFICATIONS
- Bachelor’s degree in computer science or equivalent with 8+ years of relevant working experience, or Master’s degree in computer science or equivalent with 5+ years of working experience- Experiences related to machine learning, deep learning, NLP, CV, GNN, or distributed training
- Experiences related to AWS services such as SageMaker, EMR, S3, DynamoDB and EC2
- Working knowledge of generative AI and hands on experience in prompt engineering, deploying and hosting Large Foundational Models
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