Software Dev Engineer II, EE Replenishment - Bengaluru | Amazon Job

Job Description

As a Software Development Engineer on the Everyday Essentials Promotions (EEP) team, you'll build systems that power promotional experiences for millions of Amazon customers.
Our platform determines which promotions customers see, manages discount optimization through ML driven recommendations, and processes billions of dollars in promotional spend across multiple marketplaces.

You'll work across a full stack system spanning React frontends, Java/Spring backend services, DynamoDB, SQS based async processing, and ML integration pipelines, all operating at Amazon retail scale.

We're looking for engineers who are comfortable owning ambiguous problems end to end, can design systems that balance correctness with performance, and thrive in a team that ships frequently to production.

Key job responsibilities
- Design and implement distributed backend services processing promotional configurations at scale
- Build and maintain React based portal experiences used by internal business stakeholders
- Own features end to end: from design through implementation, testing, deployment, and operational excellence
- Drive engineering best practices including code quality, testing strategy, and operational readiness
- Collaborate with science teams on ML recommendation pipelines and data driven optimization
- Participate in on call rotation and drive improvements to system reliability

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
- Experience programming with at least one software programming language
- Bachelor's degree in computer science or equivalent

Preferred Qualifications:

- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques

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