Drive revenue, adoption, and growth from small- to enterprise-level customers, including public sector, by applying cutting-edge Generative AI algorithms to solve real-world problems with significant impact.
Key Responsibilities:
- Collaborate with applied and data scientists to build robust Generative AI solutions for business problems.
- Effectively use Foundation Models on Amazon Bedrock and Amazon SageMaker to meet customer performance needs.
- Build scalable cloud environments for customers to label data, build, train, and deploy models.
- Interact with customers to understand business problems and aid in implementation of ML ecosystems.
- Analyze large amounts of historical data to automate and optimize key processes.
- Work with account teams, applied/data scientist teams, and product engineering teams to drive model implementations and new algorithms.
- Mentor junior team members.
About the Team:
- Diverse Experiences: AWS values diverse experiences and encourages candidates to apply, even if they don't meet all qualifications.
- Why AWS: Amazon Web Services is the world's most comprehensive cloud platform, trusted by successful startups and Global 500 companies.
- Inclusive Team Culture: AWS fosters a culture of inclusion, empowering employees to be proud of their differences.
- Mentorship & Career Growth: AWS provides endless knowledge-sharing, mentorship, and career-advancing resources.
- Work/Life Balance: AWS values work-life harmony, striving for flexibility in the workplace.
Basic Qualifications:
- Bachelor's degree in computer science or equivalent with 5+ years of relevant experience.
- Experience with machine learning fundamentals, Python, and deep learning frameworks like PyTorch, TensorFlow, or JAX.
- 5+ years of experience in developing and deploying large-scale machine learning or deep learning models.
Preferred Qualifications:
- Bachelor's degree in computer science or equivalent with 8+ years of experience, or Master's degree with 5+ years of experience.
- Experience with machine learning, deep learning, NLP, CV, GNN, or distributed training.
- Experience with AWS services like 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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