Senior Applied Scientist, AI, Applied Science and AI, GCF ACES
Amazon
DESCRIPTION
The ACES Applied Science and AI team is seeking a Senior Applied Scientist to lead the development of Generative AI (GenAI) products that will solve the most complex problems in the operations space. The role will focus on designing, developing, and deploying GenAI products at scale that will simplify the operations planning process, automate manual tasks, and facilitate better decision making for our operators at Fulfillment Centers (FCs).
The Applied Scientist candidate will have deep expertise in Natural Language Processing (NLP), Large Language Models (LLMs), and extensive experience with AWS technologies. This role requires someone who can work independently in an ambiguous environment while collaborating with cross-functional teams to drive innovation in the GenAI space. A successful candidate is someone who is an excellent communicator and can explain complex technical concepts to various audiences, can frame loosely defined problems into well-defined GenAI business problems, and is a strategic thinker who can balance research innovation with business requirements and product delivery.
Key job responsibilities
* Lead the development of enterprise-grade GenAI solutions, using LLMs, embedding models, and multi-modal AI systems.
* Design and implement scalable architectures for GenAI applications using AWS services in a production environment.
* Closely collaborate with program and product teams to identify and implement novel GenAI use cases.
* Develop and implement evaluation frameworks for GenAI models.
* Author technical papers and present at internal/external conferences.
* Mentor junior scientists and contribute to the team's technical strategy.
About the team
The ACES Applied Science and AI (ScAI) team delivers Generative AI, Machine Learning, and Causal Analysis solutions to solve business problems across ACES. From predictive modeling to automation, we deliver impactful solutions to enhance business efficiencies, improved decision-making, simplify operational complexity while driving measurable results.