Software Dev Engineer II, Finance Technology
Amazon · Hyderabad · onsite · Posted 2d ago
About a minute. You'll make an account on the way through.
About this role
At Amazon, we hire the best minds in technology to innovate and build on behalf of our customers. Our Software Development Engineers (SDEs) use innovative technology to solve complex problems and get to see the impact of their work first-hand. The challenges SDEs solve for at Amazon are big and influence millions of customers, sellers, and products around the world. We are looking for individuals who are passionate about creating new products, features, and services from scratch while managing ambiguity and the pace of a company where development cycles are measured in weeks, not years.
Key job responsibilities:
- Collaborate with experienced cross-disciplinary Amazonians to conceive, design, and bring innovative products and services to market.
- Design and build innovative technologies in a large distributed computing environment and help lead fundamental changes in the industry.
- Create solutions to run predictions on distributed systems with exposure to innovative technologies at incredible scale and speed.
- Build distributed storage, index, and query systems that are scalable, fault-tolerant, low cost, and easy to manage/use.
- Design and code the right solutions starting with broadly defined problems.
- Work in an agile environment to deliver high-quality software.
Basic Qualifications:
- 3+ years of programming using a modern programming language such as Java, C++, or C#, including object-oriented design experience
- Experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations
- Experience designing or architecting (design patterns, reliability and scaling) of new and existing systems
Preferred Qualifications:
- Bachelor's degree in computer science or equivalent
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience debugging, profiling, and implementing best software engineering practices in large-scale systems
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