Software Development Engineer, Amazon Private Brands, Automated Product Planning and Governance

Amazon Private Brands is building the next generation of AI-powered product planning and governance tools, and we need engineers who thrive in fast-paced, ambiguous environments to help us get there. The Product Planning and Governance Tech team within Private Brands Tech owns the systems that take a product idea from concept through compliance: automated assortment planning, business case evaluation, supplier quoting and qualification, claims management, certification validation, and regulatory intelligence.

We're hiring an SDE to join a focused tiger team that will operate in hackathon-style sprints to rapidly prototype, validate, and ship new capabilities across our AI agent platform. You'll work across the full stack, designing and building LLM-powered agents, integrating with internal and third-party PLM systems, and shipping production features that directly impact how Amazon develops private brand products globally.

• Key job responsibilities
• Design, build, and ship production services and AI-powered agents that automate product development workflows (planning, compliance, supplier management)

• Collaborate closely with product managers and scientists to translate ambiguous customer problems into working software on compressed timelines
• Build integrations between internal platforms and third-party systems used for product lifecycle management

Own end-to-end delivery: architecture, implementation, testing, deployment, and operational excellence

Contribute to a shared agent platform spanning claims evaluation, regulatory intelligence, bill of materials management, and certification auto-validation

• Iterate rapidly, working in short cycles to validate ideas with stakeholders and pivot based on feedback
• Write clean, maintainable code and raise the bar on engineering practices for the team

• A day in the life
• This role starts on a dedicated tiger team doing rapid hackathon-style development: short cycles, high autonomy, direct access to stakeholders, and fast iteration. Over time, the role will transition to operating as a standard SDE building and scaling the features that emerge from this work, owning them through production readiness and long-term operational health. If you love moving fast, building AI-powered systems, and working at the intersection of product development and engineering, this is the team for you.

• About the team
• The Product Planning and Governance Tech team sits within Amazon Private Brands Tech and owns the technology that powers how Amazon plans, qualifies, and governs its private brand product portfolio, including brands like Amazon Essentials and Amazon Basics. Our mission is to automate the end-to-end product development pipeline: from identifying what to build, through supplier qualification and business case approval, to ensuring every product meets safety, sustainability, and compliance requirements before it reaches customers.

We're a team of builders who care about shipping real impact. Our culture is hands-on, collaborative, and biased toward action. We operate across two engineering pods, one focused on assortment planning and supplier workflows, and the other on product governance (claims, certifications, requirements, regulatory intelligence), with heavy investment in AI/ML agents and LLM-based automation. Our tech stack spans Java, Python, Rust, TypeScript/CDK, React, and AWS services including Bedrock, DynamoDB, Step Functions, and Lambda.

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

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
• - Usage of generative AI tools to enhance workflow efficiency, with a willingness to learn effective prompting and evaluation practices
• - Ability to recognize opportunities where generative AI could enhance products, workflows, or customer experiences

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