Software Development Engineer, Ideas

Amazon Amazon · Big Tech · Seattle, WA · Software Development

Software Development Engineer role focused on building agentic AI solutions and data systems for AWS Sales field intelligence. The role involves designing and developing scalable data ingestion, distributed systems, and applying ML to solve data problems. Key responsibilities include building foundations for RAG systems, recommendation engines, and predictive models, with an emphasis on ML workflows and real-time inference.

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. Work with AWS technologies such as Glue, EMR, S3, DynamoDB, Redshift, ElasticSearch, Lambda, SQS, SNS, API Gateway and more

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
  • 1+ years of software development engineer or related occupational experience
  • 1+ years of Object Oriented Design experience
  • Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field

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 with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
  • Knowledge of AWS platforms such as S3, Glue, Athena, Sagemaker

What the JD emphasized

  • agentic AI development

Other signals

  • design and deliver solutions that combine distributed big data technologies and agentic AI development
  • Apply Machine Learning to solve challenging data problems at scale
  • Design data systems optimized for machine learning workflows, including feature stores, model training pipelines, and real-time inference capabilities
  • Partner with applied scientists and ML engineers to build robust data foundations for RAG systems, recommendation engines, and predictive models