Software Development Engineer, Data Integration AI and Platform Excellence (APEX)

Key job responsibilities

• Design, develop, and implement AI/ML-powered products for data integration workflows which simplifies complexity in data processing.
• Build and own critical infrastructure powering Amazon's next-generation AI capabilities
• Collaborate with data scientists and ML experts to transform innovative ideas into production systems
• Optimize existing algorithms and systems for performance, scalability, and reliability
• Contribute to the full software development lifecycle, from conception to production deployment
• Mentor junior engineers and promote best practices in software development and AI implementation

About the team

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.

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
• 1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
• Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
• Experience programming with at least one software programming language
• Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution

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
• Knowledge of machine learning model architecture and inference