Amazon is seeking an experienced, self-directed machine learning data engineer to support the research and analytical needs of Amazon Web Services' Sales teams.
This is a unique opportunity to invent new ways of leveraging our large, complex data streams to automate sales efforts and to accelerate our customers' journey to the cloud.
This is a high-visibility role with significant impact potential.
About AWS :
Amazon Web Services (AWS) provides companies of all sizes with an infrastructure web services platform in the cloud ( cloud computing ).
With AWS you can requisition compute power, storage, and many other services gaining access to a suite of elastic IT infrastructure services as your business demands them.
AWS is the leading platform for designing and developing applications for the cloud and is growing rapidly with hundreds of thousands of companies in over 190 countries on the platform.
About you :
You, as the right candidate, are adept at productionalizing and scaling innovative, high-performance machine learning (ML) and statistics solutions that solve business problems at massive scale.
You have carried projects from proof-of-concept to deployment and prediction serving with a high standard for model maintenance and operations.
You have turned large volumes of structured and unstructured data into training and prediction sets. You have orchestrated complex and / or distributed modeling systems to unlock new ML capabilities for your customers.
You have a strong grasp of popular machine learning and data processing frameworks. You have developed maintainable, reusable components and microservices as part of your pipelines that unlock speed, scale, and integration for others.
You are talented at communicating technical concepts clearly to stakeholders in concise, non-technical language.
GSO2024
Key job responsibilities
What you will do :
- Work with a team of business analysts, data scientists, and engineers to bring ML and statistics solutions to our Sales teams
- Develop and deploy models and pipelines that scale
- Use AWS services like SageMaker, EMR, ECS, Glue, and more to build and deploy scalable machine learning models in the cloud
A day in the life
Example projects :
- Scaling out and deploying regression, classification, and recommendation models
- Developing reusable cloud infrastructure and deployment patterns to accelerate productionalization
- Integrating disparate ML solutions into cohesive customer experiences
About the team
About AWS
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the preferred 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 we strive for flexibility as part of our working culture.
When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
About 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 public sector.
BASIC QUALIFICATIONS
- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience programming with at least one software programming language
PREFERRED QUALIFICATIONS
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
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