Senior AIOps Engineer - Agent Platform Lead

Early Warning; Scottsdale (AZ); Hybrid; USD 124,000 - 165,000; Full time

Benefits offered by this job

Healthcare Coverage

401(k) with match

Paid Time Off

Paid parental leave

Wellness benefits

Job summary

Early Warning is seeking a Staff Software Engineer for AIOps in a hybrid role across multiple U.S. cities. You will design, build, and operate the agent platform that powers AI-driven observability, diagnoses, and automated actions with strong safety controls.

You’ll collaborate across Engineering, Tech Ops, Security, and Risk to establish reusable patterns, guardrails, and scalable orchestration for production environments.

Qualifications

• Bachelor's degree in CS/Engineering or related field.
• 8+ years of software/ platform engineering experience.
• Strong ability to design maintainable, testable systems.
• Experience with distributed systems and enterprise controls.
• Proven ability to work with cross-functional teams.

Responsibilities

• Design and operate the core agent runtime and tool interfaces.
• Define and monitor telemetry, observability, and state management.
• Build and secure production agent pathways with safety controls.
• Collaborate across Engineering, Operations, Security, and Risk.
• Lead design decisions for scalable AIOps platform capabilities.

Skills

Python production

Distributed systems design

Front-end frameworks

TypeScript or Python API development

Strong communication

Auditability and controls design

Education

Bachelor's degree in Computer Science, Engineering, or related field

Tools

Kafka

OpenTelemetry

Prometheus

Grafana

Kubernetes

CI/CD tooling

ITSM platforms

Job description

Early Warning is seeking a Staff Software Engineer for AIOps in a hybrid role across multiple U.S. cities. You will design, build, and operate the agent platform that powers AI-driven observability, diagnoses, and automated actions with strong safety controls.

You’ll collaborate across Engineering, Tech Ops, Security, and Risk to establish reusable patterns, guardrails, and scalable orchestration for production environments.