AmazonPosted 4d ago

Software Development Engineer II, FBA Capacity and Inventory Health Management

Software Development Engineer II, FBA Capacity and Inventory Health Management at Amazon scores 17 out of 100 on AI centrality, which makes it AI Level 1 of 4 (Little AI) on this board. The level measures how much of the work is AI, not seniority.

IN, KA, Bengalurumidfull-time

AI in this role

Sellers are a critical part of Amazon’s ecosystem to deliver on our vision of offering the Earth’s largest selection and lowest prices. These Sellers offer hundreds of millions of unique products providing a broad and diverse inventory of products from Books, Electronics and Apparel to Consumables and beyond. Fulfillment By Amazon enables Sellers to provide fast and efficient delivery to their customers using Amazon fulfillment services which stores, packs, ships and provides customer service for seller products. Sellers now make up the majority of the business on Amazon.com. The FBA team is responsible for the science, business, and operations of the core systems, algorithms, and seller-facing tools and workflows of the FBA service. These enable FBA sellers to enroll selection, plan and inbound product, and manage and action inventory in Amazon fulfillment network - managing through the full inventory lifecycle. FBA Capacity Management and Planning organization builds and manages tools worldwide for FBA capacity allocation, market-driven additional limit system, capacity management in emerging countries, inventory planning and forecasting, and the north-star seller-facing metric, the Inventory Performance Index.Our systems are built entirely in-house, and are scalable for automating large-scale business, inventory planning and optimization. We work with some of the best research scientists and software developers in the industry.

Key job responsibilities
You will build Native-AI SDLC solutions for Amazon's WW selling partners. You design at the product level with heavy involvement through all phases of the SDLC using concepts that may include Service Oriented Architecture, caching, load balancing, non-relational databases, microservices, sharding, n-tiered architecture, cloud and/or map-reduce.
You use object-oriented design concepts to program large multi-tier systems. You have working knowledge of common and useful design patterns.
You have designed, refactored, re-architected, deprecated and/or implemented large-scale cloud-based distributed services with millisecond latency and ultra-high transactional volumes.
You have experience implementing best practices in improving team coding culture subjected related to quality and/or security
Effectively articulate technical challenges and solutions to non-technical audiences.

In this job, you will…
You will build Native-AI SDLC solutions for Amazon's WW selling partners.
Experiment with whatever technology we need to get the job done.
Use Lean Software Development concepts such as Agile, Scrum and Kanban.
Use Test-Driven Development and Continuous Deployment.
Work with Senior and Principal Engineers, Researchers, and Data Engineers across multiple teams to design a new platform for optimizing supply chain management
Expand and optimize our world-class supply chain systems to support a variety of emerging business initiatives.
Automate third party supply management systems and break new ground to drive inventory across Amazon marketplaces.

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
- 3+ years of Video Games Industry (supporting title Development, Release, or Live Ops) 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
- Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques

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