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 applying ML techniques like forecasting, classification, and anomaly detection, including Generative AI and LLMs.

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. work with massive datasets in the terabytes range, developing robust 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 in processing data with a massively parallel technology (such as Redshift, Teradata, Netezza, Spark or Hadoop based big data solution)

What the JD emphasized

  • machine learning applications that process and analyze transactions worth billions of dollars a day
  • drive wider adoption of machine learning solutions for finance processes
  • detect fraudulent transactions/vendors by applying machine learning techniques

Other signals

  • building machine learning applications that process and analyze transactions worth billions of dollars a day
  • drive wider adoption of machine learning solutions for finance processes
  • detect fraudulent transactions/vendors by applying machine learning techniques