Are you passionate about Artificial Intelligence, Machine Learning and Deep Learning? Are you passionate about helping customers build solutions leveraging the state-of-the-art AI/ML/DL tools on Amazon Web Service (AWS)? Come join us!
At Amazon, we’ve been investing deeply in artificial intelligence for over 20 years, and many of the capabilities customers experience are driven by machine learning. Amazon.com’s recommendations engine is driven by machine learning (ML), as are the paths that optimize robotic picking routes in our fulfillment centers. Our supply chain, forecasting, and capacity planning are also informed by ML algorithms. Alexa is fueled by Natural Language Understanding and Automated Speech Recognition deep learning; as is our drone initiative, Prime Air, and the computer vision technology in our new retail experience, Amazon Go. We have thousands of engineers at Amazon committed to machine learning and deep learning, and it’s a big part of our heritage.
Within AWS, we’re focused on bringing that knowledge and capability to customers through three layers of the AI stack: 1) Frameworks and Infrastructure with tools like Apache MxNet and TensorFlow, 2) Machine Learning Platforms such as Amazon SageMaker for data scientists and 3) API-driven Services like Amazon Lex, Amazon Polly, Amazon Transcribe, Amazon Comprehend, and Amazon Rekognition to quickly add intelligence to applications.
AWS is looking for a Machine Learning Solutions Architect (ML SA), who will be the Subject Matter Expert (SME) for helping customers in the US design solutions that leverage the first and second tiers of our ML stack, ML Frameworks/Infrastructure and ML Platforms like Amazon SageMaker. You will partner with Solution Architects, Sales, Business Development and the AI Service teams to enable customer adoption and revenue attainment for Amazon SageMaker and Machine Learning/Deep Learning in the US. You will develop white papers, blogs, reference implementations, labs, and presentations to evangelize AWS AI design patterns and best practices for Machine Learning in Amazon SageMaker and the AWS ML platform.
Your roles and responsibilities will include:
· Work with US customers’ development and data science teams to deeply understand their business and technical needs and design ML solutions that make the best use of Amazon SageMaker and other AWS Machine Learning platforms.
· Work with US customers to optimize their machine learning and deep learning models in Amazon SageMaker and the AWS ML platform.
· Thought Leadership – Evangelize AWS ML platforms in the US and share ML & SageMaker best practices through forums such as AWS blogs, whitepapers, reference architectures and public-speaking events such as AWS Summit, AWS re:Invent, etc.
· Partner with SAs, Sales, Business Development and the AI Service teams to accelerate customer adoption and revenue attainment in the US of Amazon SageMaker and other AWS ML Platforms.
· Act as a technical liaison between customers and the AWS machine learning engineering teams to provide customer driven product improvement feedback.
· Develop and support an AWS internal community of machine learning subject matter experts in the US.
· 5+ years working as a data scientist.
·5+ years design/implementation/consulting experience of Machine Learning/AI/Deep Learning solutions.
· Technical degree with statistical fundamentals required.
· Professional experience architecting/operating solutions built on AWS or equivalent cloud
· Experience communicating effectively across internal and external organizations, for complex mission-critical solutions
· Experience with predictive analytics, semi- and unstructured data
· Experience deploying production-grade machine learning solutions on public cloud platforms
· Data science background and experience manipulating/transforming data, model selection, model training, cross-validation and deployment at scale.
· Strong expertise with machine learning and deep learning models.
· Solid grounding in statistics, probability theory, data modeling, machine learning algorithms and software development techniques and languages used to implement analytics solutions.
· Experience using and adapting to new technologies.
· Deep experience with data modeling and Big Data solution stacks.
· Deep knowledge in enterprise IT technologies, including databases, storage, and networks
· Deep experience with one or more Deep Learning frameworks such as Apache MxNet, TensorFlow, PyTorch, Keras, Gluon, Chainer, CNTK, etc.
· Technical consulting and architecture with large-scale engagements.
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