Software Development Engineer - Ii, Fintech - Machine Learning

Amazon Amazon · Big Tech · IN, TS, Hyderabad · Software Development

Software Development Engineer II role in Amazon's FinTech group, focusing on building and deploying machine learning applications for transaction analysis, fraud detection, and financial process optimization. The role involves working with large datasets, collaborating with scientists and business teams, and utilizing Generative AI and LLMs to drive efficiencies and insights in finance operations.

What you'd actually do

  1. detect fraudulent transactions/vendors by applying machine learning techniques
  2. collaborating with customers on design to executing that design in a scalable and extensible way
  3. develop machine learning pipelines to process billions in transaction value
  4. partnering with our internal customers, so you'll interact directly with them to understand requirements and get feedback
  5. test your ideas in production environments dealing with real financial data and processes

Skills

Required

  • 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

Nice to have

  • 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
  • Experience in machine learning, data mining, information retrieval, statistics or natural language processing
  • Experience operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets, or experience in development or technical support

What the JD emphasized

  • machine learning applications
  • process and analyze transactions worth billions of dollars a day
  • machine learning solutions for finance processes
  • Generative AI and Large Language Models
  • machine learning pipelines
  • detect fraudulent transactions/vendors by applying machine learning techniques
  • machine learning scientists
  • machine learning applications
  • machine learning techniques
  • machine learning, data mining, information retrieval, statistics or natural language processing

Other signals

  • building machine learning applications
  • process and analyze transactions worth billions of dollars a day
  • drive wider adoption of machine learning solutions for finance processes
  • identifying anomalies in data
  • predicting optimal cash levels
  • utilize the newest tools and technologies such as Generative AI and Large Language Models
  • develop machine learning pipelines
  • detect fraudulent transactions/vendors by applying machine learning techniques