Software Development Engineer 2, Prime Video Playback Operations Tech, Prime Video Playback Live Operations Tech

Amazon Amazon · Big Tech · IN, KA, Bengaluru · Software Development

Software Development Engineer II role focused on building AI-driven automation systems for live video playback operations. The role involves developing intelligent systems, agentic frameworks, and operational tooling to autonomously manage live events, detect anomalies, resolve issues, and ensure playback quality. It sits at the intersection of AI/ML, distributed systems, and operational excellence, requiring systems that learn from data, make autonomous decisions, and operate at Amazon scale.

What you'd actually do

  1. Develop AI-powered operational intelligence systems that analyze telemetry data, detect anomalies, and make autonomous decisions about live event health and intervention strategies.
  2. Build agentic incident response frameworks that reduce mean time to recovery through intelligent root cause analysis and automated remediation
  3. Create scalable agentic pipelines that handle event lifecycle management—from technical on boarding and readiness validation to live execution and post-event analysis
  4. Design customer centric quality assessment systems that evaluate playback from the viewer's perspective, not just technical metrics, ensuring our interventions improve rather than disrupt the viewing experience
  5. Implement predictive analytics capabilities that identify potential failures before they occur, learning from historical patterns to prevent recurring issues

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
  • 3+ years of programming with at least one software programming language experience

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
  • Bachelor's degree or equivalent
  • Experience in machine learning, data mining, information retrieval, statistics or natural language processing

What the JD emphasized

  • AI-driven automation systems
  • intelligent systems
  • autonomous management
  • AI/ML, distributed systems, and operational excellence
  • systems that learn from operational patterns
  • make intelligent decisions
  • automatically resolve issues
  • AI-powered operational intelligence systems
  • make autonomous decisions
  • agentic incident response frameworks
  • intelligent root cause analysis
  • automated remediation
  • scalable agentic pipelines
  • customer centric quality assessment systems
  • predictive analytics capabilities
  • identify potential failures
  • learning from historical patterns
  • operational tooling and dashboards
  • real-time visibility into system health
  • AI decision-making
  • ML-enhanced detection logic
  • continuously improve the system's predictive capabilities
  • automation is autonomously managing live events
  • detecting, analyzing, and resolving issues
  • building intelligent systems
  • AI-powered autonomous operations system
  • all operational decisions are diagnosed and mitigated independently

Other signals

  • AI-driven automation systems
  • intelligent systems
  • autonomous management
  • AI/ML, distributed systems, and operational excellence
  • systems that learn from operational patterns
  • make intelligent decisions
  • automatically resolve issues
  • AI-powered operational intelligence systems
  • make autonomous decisions
  • agentic incident response frameworks
  • intelligent root cause analysis
  • automated remediation
  • scalable agentic pipelines
  • customer centric quality assessment systems
  • predictive analytics capabilities
  • identify potential failures
  • learning from historical patterns
  • operational tooling and dashboards
  • real-time visibility into system health
  • AI decision-making
  • ML-enhanced detection logic
  • continuously improve the system's predictive capabilities
  • automation is autonomously managing live events
  • detecting, analyzing, and resolving issues
  • building intelligent systems
  • AI-powered autonomous operations system
  • all operational decisions are diagnosed and mitigated independently