Allstate
Insurance · Insurance
- HQ
- Northbrook, US
- Founded
- 1931
- Website
- allstate.com
Currently tracking 7 active AI roles, down 97% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $75k–$291k (avg $148k).
Insurance · Insurance
Allstate currently has 12 active AI-related job listings. The majority of these roles, 67%, are focused on agents. Engineering is the dominant function, with 11 roles, and the majority of hiring is in the United States, with 9 positions. Frequent technology tags include agent orchestration, LLM observability, and model serving, suggesting a focus on managing and deploying AI agents. Over the last 30 days, Allstate has added 13 new AI roles, representing a 225% increase from the previous 30-day period.
Allstate currently has 12 active AI-related roles in our index. The most common open titles are: AI Digital Product Manager, AI Engineer Lead, Director, Engineer (AI, Data & Security Tooling Ecosystem), Lead AI Cloud Platform Engineer, Lead Digital Product Manager (AI, Data & Security Tooling Ecosystem). Most positions are in Engineering and Product.
Allstate's active AI hiring is concentrated in: agents (67%), application (17%), serving infrastructure (17%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Allstate is hiring AI talent in: United States (9 roles), India (1 role).
Job postings at Allstate most frequently reference: agent orchestration, llm observability, model serving, rag, guardrails.
In the past 30 days, Allstate has posted 5 new AI-related roles. That is a -62% change versus the prior 30 days (13 → 5).
| Title | Stage | AI score |
|---|---|---|
| AI Engineer Lead AI Engineer Lead at Allstate Investments responsible for guiding AI/ML initiatives from proof of concept to scalable, production-ready solutions. This role involves hands-on development with LLMs, RAG, vector stores, and agentic frameworks, partnering with product and platform leaders to build AI systems. Key responsibilities include establishing AI engineering fundamentals, participating in experimentation and productization, and iterating on PoCs. | AgentServe | 8 |
| Senior Data Scientist - Agentic AI Senior Data Scientist role focused on building and deploying data-driven agentic AI solutions, including LLM integration, cloud infrastructure, and observability, within an enterprise AI risk and governance context. | Agent | 8 |
| Senior AI Engineer Senior AI Engineer to build an enterprise Semantic Ontology & Dimension Factory Platform. This platform combines semantic ontologies, knowledge graphs, and agentic AI to generate business-ready star schemas from enterprise data. The role involves designing and implementing agent-driven pipelines using LLMs and semantic technologies (RDF/OWL, SPARQL) for data modeling and alignment, as well as building Python microservices and data pipelines on Microsoft Fabric. | AgentData | 7 |
| Director, Engineer (AI, Data & Security Tooling Ecosystem) The Principal Engineer will be the technical authority for the AI, Security Operations Data, and security tooling ecosystem underpinning Cyber Operations. This role defines and enforces engineering standards for shared AI and data capabilities, ensuring reliability, security, observability, and auditability at enterprise scale. It also supports the integration of security-relevant tooling into the ecosystem with consistent patterns and automated controls. | AgentData | 7 |
| Software Engineer (AI/GenAI Platforms) Software Engineer focused on building AI/GenAI/Agentic AI capabilities on an Unstructured Data Platform, transforming enterprise data into actionable intelligence and autonomous AI systems. Requires Python/Java, experience with LLMs, RAG, Vector Search, Multimodal AI, AI Agents, Prompt Engineering, and cloud platforms like AWS. | Agent | 7 |
| Senior AI Cloud Platform Engineer This role focuses on building and operating cloud application development and hosting platforms for GenAI, enabling developers to build, deploy, and operate AI applications. It involves developing self-service tools, managing infrastructure as a service, and integrating various AI components and APIs. | Serve | 7 |