Systems Development Engineer, Firetv S2d2 Ltpm Team

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

This role focuses on building and scaling infrastructure for Fire TV devices, with a significant emphasis on leveraging AI/ML for automation, anomaly detection, predictive alerting, and self-healing systems. The primary focus is on the engineering and deployment of these AI-enhanced systems within the infrastructure.

What you'd actually do

  1. Design and implement CI/CD pipelines for Fire TV software builds, testing, and OTA delivery
  2. Build infrastructure automation for device labs, test farms, and deployment systems
  3. Develop AI/ML-powered solutions for anomaly detection, predictive alerting, and automated remediation
  4. Create and maintain observability platforms for device metrics, crash analytics, and service health
  5. Automate operational workflows using LLMs and agentic AI tools to reduce toil

Skills

Required

  • Experience in automating, deploying, and supporting large-scale infrastructure
  • Experience programming with at least one modern language such as Python, Ruby, Golang, Java, C++, C#, Rust
  • Experience with Linux/Unix
  • Experience with CI/CD pipelines build processes
  • Knowledge of systems engineering fundamentals (networking, storage, operating systems)

Nice to have

  • Experience with distributed systems at scale
  • Experience with AI/ML operations—model deployment, inference pipelines, or MLOps
  • Hands-on experience with LLMs, prompt engineering, or agentic AI workflows
  • Knowledge of embedded systems, device firmware, or OTA update mechanisms
  • Experience with device lab automation (ADB, device farms, hardware-in-the-loop testing)
  • Familiarity with crash analytics, log aggregation, and anomaly detection systems
  • Experience building AI-assisted tooling for developer productivity or operations
  • Understanding of video streaming, media pipelines, or smart TV ecosystems
  • Apply generative AI to automate runbooks, incident triage, and documentation
  • Build intelligent alerting systems that reduce noise and surface actionable insights
  • Develop AI-powered code review, test generation, or debugging assistants
  • Create self-healing infrastructure that detects and remediates issues autonomously

What the JD emphasized

  • AI/ML-powered solutions for anomaly detection, predictive alerting, and automated remediation
  • Automate operational workflows using LLMs and agentic AI tools
  • Apply generative AI to automate runbooks, incident triage, and documentation
  • Build intelligent alerting systems that reduce noise and surface actionable insights
  • Develop AI-powered code review, test generation, or debugging assistants
  • Create self-healing infrastructure that detects and remediates issues autonomously

Other signals

  • AI/ML to enhance systems
  • AI/ML-powered solutions for anomaly detection, predictive alerting, and automated remediation
  • Automate operational workflows using LLMs and agentic AI tools
  • Apply generative AI to automate runbooks, incident triage, and documentation
  • Build intelligent alerting systems
  • Develop AI-powered code review, test generation, or debugging assistants
  • Create self-healing infrastructure