Software Dev Engineer Ii, Ideas

Amazon Amazon · Big Tech · Arlington, VA · Software Development

Software Development Engineer II role at AWS focused on building foundational customer intelligence for go-to-market teams. The role involves designing and delivering solutions that combine distributed big data technologies and agentic AI development, with a focus on data ingestion, scalable systems, and ML-optimized data foundations for RAG, recommendation engines, and predictive models. The engineer will also work with inference capabilities and apply ML to solve data problems.

What you'd actually do

  1. Design, develop, and maintain scalable data ingestion solutions to solve complex and ambiguous data challenges for large-scale datasets
  2. Build highly available, secure, distributed systems in microservices, container, and distributed cluster-compute architectures with comprehensive data validation frameworks
  3. Develop serverless applications using AWS Lambda, S3, CloudWatch, and Glue services while implementing infrastructure as code through AWS CDK for automated deployment and monitoring
  4. Apply Machine Learning to solve challenging data problems at scale
  5. Design data systems optimized for machine learning workflows, including feature stores, model training pipelines, and real-time inference capabilities

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

What the JD emphasized

  • agentic AI development
  • data systems optimized for machine learning workflows
  • RAG systems

Other signals

  • agentic AI development
  • data ingestion solutions
  • scalable data ingestion solutions
  • distributed big data technologies
  • actionable intelligence
  • data systems optimized for machine learning workflows
  • feature stores
  • model training pipelines
  • real-time inference capabilities
  • RAG systems
  • recommendation engines
  • predictive models