Deep Learning Architect, Generative AI Innovation Center

4 weeks ago

Arlington, United States Amazon Web Services, Inc. Full time
Amazon launched the Generative AI (GenAI) Innovation Center (GAIIC) in Jun 2023 to help AWS customers accelerate enterprise innovation and success with Generative AI ( Customers such as Highspot, Lonely Planet, Ryanair, and Twilio are engaging with the GAI Innovation Center to explore developing generative solutions. The Public Sector team focuses on the unique GenAI challenges and opportunities of public sector customers.

Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center at AWS is a new strategic team that helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, data scientists, engineers, and solution architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI.

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.

This position requires that the candidate selected be a US Citizen.

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. 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.

Key job responsibilities
In this role, you will:
• Use ML and Generative AI tools, such as Amazon SageMaker and Amazon Bedrock to provide a scalable cloud environment for our customers to label data, build, train, tune and deploy their models
• Collaborate with our data scientists to create and fine tune scalable ML and Generative AI solutions for business problems
• 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 team, research scientist teams and product engineering teams to drive model implementations and new algorithms

About the team

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.

We are open to hiring candidates to work out of one of the following locations:

Arlington, VA, USA | Denver, CO, USA | Washington, DC, USA

BASIC QUALIFICATIONS

- Bachelor of Science degree in Computer Science, or related technical, math, or scientific field (or equivalent experience)
- Experience coding in Python, R, Matlab, Java or other modern programming language
- 1+ year of public cloud computing experience in AWS or other large scale cloud providers
- 4+ years of experience hosting and deploying ML solutions (e.g., for training, fine tuning, and inferences)

PREFERRED QUALIFICATIONS

- Masters or PhD degree in computer science, or related technical, math, or scientific field
- Strong working knowledge of deep learning, machine learning and statistics
- Experiences related to AWS services such as SageMaker, Bedrock, EMR, S3, OpenSearch Service, Step Functions, Lambda, and EC2
- Hands on experience with deep learning (e.g., CNN, RNN, LSTM, Transformer), machine learning, CV, GNN, or distributed training
- Strong communication skills, with attention to detail and ability to convey rigorous mathematical concepts and considerations to non-experts

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