Are you passionate about solving complex problems using Artificial Intelligence? Do you enjoy finding patterns and pushing the boundaries of current possibilities? Are you interested in building high-performance, globally scalable systems that support Amazon's growth? If so, Amazon Finance Technology (FinTech) is the perfect place for you! At Fintech Fintelligence, our mission is to build AI and GenAI solutions for Amazon's Finance and Global Business Services, enhancing productivity, accelerating decision-making, and automating workflows that depend on specialized human intelligence and heuristics. Our focus is on rapid execution, advanced scaling, improved auditability, and delivering an exceptional customer experience. We develop thriving AI platforms that require expertise in traditional and generative ML techniques, computer science fundamentals, statistical methods, and coding skills. Continuously innovating, we add new services to address real-world problems through research and innovation, building state-of-the-art services using the latest deep learning techniques and highly scalable distributed systems that are both accurate and fast. Key job responsibilities We are looking for a hands-on researcher, who wants to derive, implement, and test the next generation of Generative AI algorithms (including Diffusion Models, auto-regressors, VAEs, or other generative models). The research we do is innovative, multidisciplinary, and far-reaching. We aim to define, deploy, and publish cutting edge research. In order to achieve our vision, we think big and tackle technology problems that are cutting edge. Where technology does not exist, we will build it. Where it exists we will need to modify it to make it work at Amazon scale. We need members who are passionate and willing to learn. * Derive novel ML or Computer Vision or LLMs and NLP algorithms. Demonstrate thorough technical knowledge on feature engineering of massive datasets, effective exploratory data analysis, and model building using industry standard time Series Forecasting techniques and formulate ensemble model. * Work with very large datasets. Proficiency in both Supervised(Linear/Logistic Regression) and UnSupervised algorithms(k means clustering, Principle Component Analysis, Market Basket analysis). * Work closely with software engineering teams and Product Managers to deploy your innovations. Understand the business reality behind large sets of data and develop meaningful solutions comprising of analytics as well as marketing management. * Design and develop scalable ML solutions. Exposure at implementing and operating stable, scalable data flow solutions from production systems into end-user facing applications/reports. These solutions will be fault tolerant, self-healing and adaptive. * Publish your work at major conferences/journals. Detail-oriented and must have an aptitude for solving unstructured problems. You should work in a self-directed environment, own tasks and drive them to completion. * Mentor team members in the use of your Generative AI and LLMs.. Excellent business and communication skills to be able to work with business owners to develop and define key business questions and to build data sets that answer those questions A day in the life In a typical day as a applied scientist at Amazon FinTech, you'll begin by delving into massive datasets, applying your technical expertise in feature engineering and exploratory data analysis to uncover valuable insights. You'll utilize both supervised and unsupervised machine learning algorithms, such as linear regression and k-means clustering, to build predictive models and solve complex optimization problems like inventory and network optimization. Collaboration with business, engineering, and partner teams is essential, as you'll translate data-driven findings into actionable insights that align with strategic goals. Throughout the day, you'll innovate by adapting new modeling techniques, ensuring data flow solutions are stable, scalable, and fault-tolerant. Your strong communication skills and attention to detail will help you manage and integrate large datasets, solving unstructured problems and driving projects to completion in a fast-paced, dynamic environment. About the team We are a tight-knit group that shares our experiences and help each other succeed. We believe in team work. We love hard problems and like to move fast in a growing and changing environment. We use data to guide our decisions and we always push the technology and process boundaries of what is feasible on behalf of our customers. Join us and be a part of our dynamic team, driving the future of financial technology at Amazon.
October 04, 2024Rufus leverages AWS chips Trainium and Inferentia, AWS’s elasticity and scalability, and a custom-built large language model to quickly answer shoppers’ questions.
