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Your Role:In this role, you will:
- Leverage Advanced AI Techniques: Utilize and understand machine learning techniques, prompt engineering, and agent-based workflows. Your role will require a blend of research acumen and practical engineering skills.Key job responsibilities- Research and Engineering Integration: Apply the latest findings in machine learning and AI to practical software development challenges. Your work will be a blend of research and real-world application.- Innovate and Implement AI Solutions: Be actively involved in the ideation and implementation of new features, leveraging your understanding of AI and machine learning.
- Ensure High-Quality Software Standards: Uphold our high standards in software development, focusing on the reliability, scalability, and performance of our AI-powered tools.
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.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
- 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
- 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
- Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence
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