Are you passionate to help customers accelerate their journey with Machine Learning and Cloud Computing? The AWS Machine Learning Product Management team is looking for an expert Machine Learning Architect with expertise in designing ML solutions to enable rapid adoption by customers. The ML Architect will be the Subject Matter Expert (SME) for helping enterprise customers design machine learning solutions that leverage the Amazon SageMaker on AWS. You will also partner with target ISV partners to develop deeper technical integration with Amazon SageMaker. You will partner with field SAs, Sales, Business Development and the ML Service teams to enable data migration and rapid adoption of Machine learning services. You will develop migration playbooks, reference implementations and share best practices with global community of ML specialists.
You will have the opportunity to help shape and execute a strategy to build mindshare and broad use of AWS within startups and enterprise customers. The ideal candidate must be self-motivated with a proven track record in machine learning and solution architecture. You should be technically adept to complement customer teams in their adoption of AWS ML services. You should also have a demonstrated ability to think strategically about business, products, and technical challenges.
Roles and Responsibilities
· Work with customer’s ML team to deeply understand their business and technical needs and design ML solutions that make the best use of the AWS Cloud platform and ML services.
· Partner with Generalist SAs, Sales, Business Development and the AI Service teams to accelerate customer adoption and revenue attainment.
· Act as a technical liaison between customers and the service engineering teams to provide product enhancement feedback.
· Develop and support an AWS internal community of AI related subject matter experts.
· Capture and share best-practice knowledge amongst the AWS solutions architect community
· Build deep relationships with senior technical individuals within customers to enable them to be cloud advocates
· Act as a technical liaison between customers, service engineering teams and support
· Thought Leadership – Evangelize AWS Services and share best practices through forums such as AWS blogs, whitepapers, reference architectures and public-speaking events such as AWS Summit, AWS re:Invent, etc.
· Open to domestic travel up to 30%
Inclusive Team Culture
Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.
Mentorship & Career Growth
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
· Deep understanding and experience in the field of AI, Machine Learning, Deep Learning and related technologies.
· Experience developing ML models in real-world environments and integrating AI/ML and other AWS services into large-scale production applications.
· Deep understanding of AWS Services such as S3, ECS, EKS and EMR.
· 2+ years professional experience in software development in languages like Java, Python, Scala. Experience working with RESTful API and general service-oriented architectures
· 4+ years design/implementation/consulting experience with Enterprise applications
· 3+ years experience in infrastructure and network architecture, DevOps, or software engineering
· 1+ years relevant experience in technology/software sales
· Technical degree required; Computer Science or Mathematics background highly desired
· Data science background and experience manipulating/transforming data, model selection, model training, cross-validation and deployment at scale.
· Experience with Machine and Deep Learning toolkits such as MXNet, TensorFlow, Caffe and Torch.
· Experience with AWS services related to AI/ML highly desirable, particularly Amazon EMR, AWS Lambda, Machine Learning, IoT, Amazon DynamoDB, Amazon S3 etc.
· 2+ years design/implementation/consulting experience building cloud solutions using AWS.
· Experience migrating or transforming legacy customer solutions to the cloud
· Professional experience architecting/operating solutions built on AWS
· Working knowledge of software development tools and methodologies
· Presentation skills with a high degree of comfort speaking with executives, IT Management, and developers
· Strong written communication skills
· High level of comfort communicating effectively across internal and external organizations
· Demonstrated ability to adapt to new technologies and learn quickly
Amazon is committed to a diverse and inclusive workforce.View More
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