Lead AI Observability Engineer

allstate

Illinois

Remote

USD 100,000 - 171,000

Full time

14 days+

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Job summary

Allstate is seeking a forward-thinking Lead Product Engineer to drive innovation in its AI-powered Observability platform. You will lead the design and development of next-generation observability solutions powered by agentic AI, enabling proactive detection, diagnosis, and remediation of issues.

This role emphasizes scalable platforms, intelligent automation, and developer-centric tooling that enhance reliability and performance.

Qualifications

5+ years of engineering experience building software, platforms, or automation solutions with a focus on observability and distributed systems.
4+ years with observability platforms (Datadog, Dynatrace, OTEL).
3+ years of software development using Java (Spring Boot), Python, Node.js, or React.
2+ years building agentic AI systems (autonomous agents, AI workflows, or AI-native apps).
Proven ability to design AI-driven automation and AIOps solutions.
Experience integrating LLMs, orchestration frameworks, or AI pipelines into production systems.
Strong experience with Kubernetes and cloud-native architectures.
Hands-on experience in hybrid environments (on-prem + cloud) across Linux and Windows.
Expertise in API development, event-driven systems, and microservices architecture.
Familiarity with CI/CD pipelines, infrastructure-as-code, and DevOps practices.
Strong problem-solving skills with a focus on automation and innovation.
Excellent communication skills to articulate complex technical solutions.
Passion for emerging technologies, particularly AI in observability and operations.

Responsibilities

Design and build agentic AI solutions for observability, including autonomous agents capable of anomaly detection, root cause analysis, and automated remediation.
Embed AI/ML capabilities into observability platforms to enable predictive insights, anomaly detection, and intelligent alerting.
Develop AIOps frameworks that reduce noise, automate workflows, and improve incident response times.
Leverage LLMs and AI orchestration frameworks to create self-service diagnostics and operational assistants.
Architect and implement end-to-end observability solutions across logs, metrics, traces, and events.
Enhance observability platforms with custom integrations, automation, and AI enhancements.