AI & Aws Software Development Engineer , Ring/blink Cs Technology Enablement

Amazon Amazon · Big Tech · Hawthorne, CA · Software Development

Software Development Engineer to design and deliver key components of an AI-powered customer service platform, focusing on automated support systems and integrating AWS AI services. The role involves end-to-end technical design, implementation, optimization, and collaboration across teams to scale solutions handling millions of interactions with sub-second response times, while ensuring security and compliance with regulations like PCI DSS and HIPAA.

What you'd actually do

  1. Research and evaluate AWS AI services (Bedrock, Amazon Q, SageMaker, Comprehend, Lex, Polly, Transcribe) and Amazon Connect AI features for customer service applications
  2. Own end-to-end technical design and implementation of major features within our AI initiatives and AWS infrastructure, from initial concept through production deployment
  3. Build robust, scalable solutions for customer support automation that handle millions of interactions with sub-second response times
  4. Design security architecture and controls for AI/ML systems, ensuring compliance with contact center regulations (PCI DSS, HIPAA) and data privacy requirements
  5. Implement core features of our AI-powered customer service platforms using cutting-edge machine learning and natural language processing technologies

Skills

Required

  • Software Development
  • AI/ML
  • AWS services (Bedrock, Amazon Q, SageMaker, Comprehend, Lex, Polly, Transcribe, Connect)
  • Customer service automation
  • System design and architecture
  • Scalability and performance optimization
  • Security and compliance (PCI DSS, HIPAA)
  • Machine learning model lifecycle management
  • Natural Language Processing

Nice to have

  • Proof of concepts and pilot programs
  • Code reviews
  • Technical mentoring
  • Reusable solutions and reference architectures
  • Technical content creation

What the JD emphasized

  • critical features
  • key architectural decisions
  • scalable solutions
  • productionize ML models
  • AI model performance optimization
  • end-to-end technical design and implementation
  • scalable, secure, reliable, and cost-optimized solutions
  • robust, scalable solutions
  • sub-second response times
  • high quality and reliability
  • performance monitoring
  • system health management
  • architectural improvements
  • failover and recovery
  • data replication
  • scaling bottlenecks
  • latency
  • security
  • cost
  • security, privacy, legal, and operational standards
  • security architecture and controls
  • compliance with contact center regulations (PCI DSS, HIPAA)
  • data privacy requirements
  • security best practices
  • high code quality standards
  • reusable solutions and reference architectures

Other signals

  • AI-powered customer service platform
  • intelligent systems
  • automated support systems
  • productionize ML models
  • AI model performance optimization
  • AI initiatives
  • AI technologies
  • AI-driven automations
  • AI systems
  • AI applications
  • machine learning
  • natural language processing