Infra Reliability SDE: AI-Powered Incident Platform

Amazon

Nashville (TN)

On-site

USD 137,000 - 185,000

Full time

8 days ago

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Benefits offered by this job

Medical, Dental, and Vision Coverage

Maternity and Parental Leave Options

Paid Time Off (PTO)

401(k) Plan

Job summary

Amazon.com Services LLC in Nashville, TN is hiring Reflex, the agentic incident-response platform for Amazon's fulfillment network. You'll design and ship production software and AI agents on Amazon Bedrock AgentCore to triage high-severity incidents and generate real-time call intelligence.

Join a software team in Operations Infrastructure Services, stay close to live operations, and help mature Reflex toward shared incident context across organizations with guarded automation of recovery and

Qualifications

3+ years of non-internship professional software development experience.
2+ years of non-internship design or architecture of new and existing systems experience
1+ years of software development engineer or related occupational experience
1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using languages such as C#, C++, Java, or Perl
1+ years of Object Oriented Design experience
Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
Experience programming with at least one software programming language

Responsibilities

Design, build, test, deploy, and operate production services and AI agents on AWS (Amazon Bedrock AgentCore, serverless compute, event-driven pipelines) that automate incident triage, call intelligence, communications, and post-incident documentation and reporting
Own features end-to-end: from discovery with Incident Managers and resolver teams, through design, implementation, evaluation, deployment, and production operation
Build the foundations that gate agent autonomy: LLM output evaluation, observability and alerting for agents in production, and identity and access controls aligned with Amazon standards
Design the feedback loops through which agents learn: capturing human reviews, corrections, approvals, and incident outcomes as evaluation signal, and turning resolved incidents into structured history that improves recommendations over time
Integrate with the incident lifecycle (ticketing, chat, telemetry, detection feeds, and live call transcription) and model consistent incident state across those systems
Raise the bar on software quality, security, testing, and operational excellence for AI systems acting inside production incident workflows