Lead AI-Driven Observability Engineer (AIOps)

Allstate; United States; Remote; USD 100,000 - 171,000; Full time

Benefits offered by this job

Connectivity reimbursement

Tech equipment provided

Job summary

Allstate is seeking a forward-thinking Lead Product Engineer to drive AI-powered observability across hybrid and multi-cloud environments. You will design and build agentic AI solutions, embed ML capabilities, and lead end-to-end observability initiatives with Java/Spring Boot, Python, Node.js, and React.

You will shape product strategy, foster engineering excellence, and deliver scalable, automated monitoring and self-healing capabilities that reduce noise and improve reliability

Qualifications

• 5+ years of engineering experience building software, platforms, or automation solutions.
• 4+ years of hands-on experience with observability platforms (Datadog, Dynatrace, OTEL).
• 3+ years of software development experience using Java (Spring Boot), Python, Node.js, or React.
• 2+ years of hands-on experience building agentic AI systems (autonomous agents, AI workflows, or AI-native applications).
• Proven ability to design and implement 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.
• Build real-time insights and health analytics to ensure system reliability, scalability, and performance.
• Lead development of scalable, reusable platform components and APIs for observability and automation.
• Build developer-first tools and frameworks that simplify instrumentation, monitoring, and diagnostics.
• Apply modern software engineering practices including TDD, CI/CD, microservices, and cloud-native design.
• Develop full-stack solutions using Java/Spring Boot, Python, Node.js, and React.
• Drive automation-first strategies to eliminate manual operations and improve efficiency.
• Build self-healing systems that automatically detect and remediate issues.
• Lead initiatives in infrastructure-as-code, Observability-as-code and automated deployment pipelines.
• Engineer highly resilient systems focusing on performance, scalability, and fault tolerance.

Skills

Agentic AI

Observability

Java

Spring Boot

Python

Node.js

React

Kubernetes

Cloud-native

Hybrid environments

API development

CI/CD

DevOps practices

LLMs & AI pipelines

Tools

Datadog

Dynatrace

OTEL

Job description

Allstate is seeking a forward-thinking Lead Product Engineer to drive AI-powered observability across hybrid and multi-cloud environments. You will design and build agentic AI solutions, embed ML capabilities, and lead end-to-end observability initiatives with Java/Spring Boot, Python, Node.js, and React.

You will shape product strategy, foster engineering excellence, and deliver scalable, automated monitoring and self-healing capabilities that reduce noise and improve reliability