Software Development Engineer, Alexa AI Logistics for Infrastructure, Cost, & Efficiency

Are you passionate about building infrastructure that powers AI at scale? Do you want to work on systems that serve millions of Alexa customers every day? The Alexa AI Logistics for Infrastructure, Cost, and Efficiency (ALICE) team is looking for Software Development Engineers to join our new India team and help build and operate Alexa's LLM inference infrastructure.
At ALICE, we build products and solutions that enable Alexa service owners to analyze, forecast, and manage infrastructure drivers, costs, and usage. You will be part of a founding team in India, working on high-visibility, high-impact systems that are critical to the success of Alexa's next generation of AI experiences.
Key job responsibilities
- Design, build, and maintain components of the Alexa's LLM Inference Service and other ALICE portfolio services, leveraging GenAI at all phases of the development cycle
- Collaborate with senior engineers and the SDM to deliver features from design through production deployment
- Participate in on-call rotations and contribute to the operational excellence of Tier-1 production services
- Write clean, testable, maintainable code and participate actively in code reviews
- Work closely with partner teams across Alexa to deliver cross-functional projects
- Research and identify opportunities to improve service performance, reliability, and cost efficiency
- 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 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 in computer science or equivalent
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
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