Software Development Engineer, Fintech - Machine Learning

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

Software Development Engineer role focused on building machine learning applications for FinTech, specifically for transaction analysis, anomaly detection, and fraud prevention within Amazon's finance operations. The role involves developing ML pipelines, utilizing Generative AI and LLMs, and collaborating with scientists and business teams to drive efficiencies and insights at scale.

What you'd actually do

  1. detect fraudulent transactions/vendors by applying machine learning techniques
  2. participate in all parts of the software development process, from collaborating with customers on design to executing that design in a scalable and extensible way
  3. work with terabytes of data and develop machine learning pipelines which process billions of dollars
  4. meet with customers directly to gain direct feedback on your work
  5. test your ideas in the real world

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
  • finance processes
  • Generative AI and Large Language Models
  • machine learning pipelines
  • fraudulent transactions/vendors
  • machine learning techniques
  • machine learning scientists
  • machine learning applications

Other signals

  • building machine learning applications
  • process and analyze transactions worth billions of dollars
  • 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 robust machine learning pipelines
  • detect fraudulent transactions/vendors by applying machine learning techniques