Interested in this AI/ML Engineer role at Allstate Insurance?
Apply Now →Skills & Technologies
About This Role
At Allstate, great things happen when our people work together to protect families and their belongings from life’s uncertainties. And for more than 90 years, our innovative drive has kept us a step ahead of our customers’ evolving needs. From advocating for seat belts, air bags and graduated driving laws, to being an industry leader in pricing sophistication, telematics, and, more recently, device and identity protection.
Job Description
Allstate Data \& Analytics Technology (DAT) is seeking an AI \& Cloud Platform Consultant Expert who can help design, build, and optimize the next generation of our data and analytical platform ecosystem, enabling our business and technology partners to achieve data to insights in less than 24\-hours. This role blends hands\-on engineering with strategic consulting, enabling teams across the enterprise to adopt modern development and integration practices, cloud\-ready architectures, and emerging AI capabilities.
You will partner with architecture, engineering, security, and operations teams ensuring our platforms are scalable, resilient, and aligned with Allstate’s technology strategy. This position is ideal for someone who thrives in complex environments, enjoys solving hard problems, and confident in forward/reverse engineering technical solutions while expertly managing priorities, value, and time to achieve measurable impact to business outcomes.
Your initial focus will be partnering with our Enterprise Architecture (EA) and DAT product engineering teams on designing, operationalizing and scaling our Azure Fabric, PowerBI, ML and AI/Foundry platform components, living into our Data\-in\-Place strategy, providing data to insights in less than 24 hours through human/agentic solutions. The longer\-term focus will be architecting and engineering a multi\-cloud strategy/solution in GCP and AWS.### Key Responsibilities:
- Act as a trusted technical advisor to engineering and product teams on platform architecture, modernization, and best practices; build credibility and influence with engineering and digital product leadership to shape backlogs, standards, and engineering practices.
- Provide strategic direction for platform evolution, ensuring business and technology roadmaps are aligned both tactically and strategically, from near‑term delivery through long‑term capability development.
- Design, prototype, and evolve core platform components, contributing to the broader enterprise architecture and enabling engineering teams with scalable, secure, and well‑documented reference solutions.
- Evaluate, design, and integrate AI/ML capabilities into platform services, including model consumption patterns, automation, and intelligent workflows that deliver measurable business value.
- Lead platform assessments and architectural reviews, conducting stakeholder alignment, risk analysis, and structured problem‑solving to support high‑priority initiatives and critical decision‑making.
- Influence Discovery, Framing, and Inception activities across the ecosystem, applying architectural simplicity and sound engineering principles to drive clear outcomes and reduce complexity.
- Apply industry standards and assess emerging technologies, synthesizing and communicating relevant insights to the broader engineering community to ensure platform solutions meet enterprise expectations.
- Collaborate closely with security, risk, and compliance teams to ensure platform architectures adhere to governance, regulatory, and security requirements by design.
- Provide technical mentorship and thought leadership, including documentation standards, and knowledge sharing across platform consultants and engineering teams.
### Required Qualifications:
- 7\+ years of progressive experience in software engineering, platform engineering, and/or solution architecture, with demonstrated impact in enterprise environments.
- Proven expertise designing and delivering cloud\-native analytical architectures on at least one major cloud platform (Microsoft Azure, AWS, or Google Cloud Platform), including production\-scale deployments.
- Strong hands\-on proficiency in at least two of the following technologies: Python, Spark/Scala, Terraform/Env0, and SQL, with the ability to apply them to real\-world platform and data engineering challenges.
- Solid understanding of cloud networking concepts, including virtual networks, routing, security boundaries, and hybrid connectivity patterns.
- Demonstrated ability to design and implement scalable, secure, and resilient data, security, and integration architectures suitable for enterprise\-grade solutions.
- Experience with DevOps and platform engineering practices, including CI/CD pipelines, infrastructure as code, Github, and automated environment provisioning.
- Excellent communication skills, with the ability to clearly articulate complex technical concepts to both technical audiences and non\-technical stakeholders.
- Strong analytical and problem\-solving skills, with comfort operating in fast\-paced, evolving environments and navigating ambiguity.
### Preferred Qualifications:
- Experience designing and scaling analytical platforms that are widely adopted by engineers, data scientists, and business analysts across an organization.
- Hands\-on experience with Azure Fabric, Azure AI services, MLOps practices, or enterprise AI integration patterns, including model deployment, monitoring, and lifecycle management.
- Working knowledge of AI/ML concepts, including model integration, inference patterns, and enabling AI‑driven features within production applications.
- Exposure to event‑driven architectures, including messaging systems, pub/sub models, or real‑time data streaming platforms.
- Background in platform governance, standards definition, or enterprise architecture, with an understanding of balancing guardrails and developer autonomy.
- Experience with modern cloud platforms (Azure preferred), including containerization and orchestration technologies such as Docker and Kubernetes.
\#LI\-TE1
Skills
AI Agents, Architectural Design, Cloud Computing, Cloud Platform, Compliance, Containerization, Design, DevOps, Distributed Systems, Enterprise Architecture, Generative AI, GitHub, Kubernetes, Machine Learning (ML), Microservices Architecture, Microsoft Azure, Performance Tuning, Platform Architecture, Platform Engineering, Python (Programming Language), Reference Architectures, Strategic SolutionsCompensation
Compensation offered for this role is 134,000\.00 \- 209,750\.00 annually and is based on experience and qualifications.
The candidate(s) offered this position will be required to submit to a background investigation.
Joining our team isn’t just a job — it’s an opportunity. One that takes your skills and pushes them to the next level. One that encourages you to challenge the status quo. One where you can shape the future of protection while supporting causes that mean the most to you. Joining our team means being part of something bigger – a winning team making a meaningful impact.
Allstate generally does not sponsor individuals for employment\-based visas for this position.
Effective July 1, 2014, under Indiana House Enrolled Act (HEA) 1242, it is against public policy of the State of Indiana and a discriminatory practice for an employer to discriminate against a prospective employee on the basis of status as a veteran by refusing to employ an applicant on the basis that they are a veteran of the armed forces of the United States, a member of the Indiana National Guard or a member of a reserve component.
For jobs in San Francisco, please click “here” for information regarding the San Francisco Fair Chance Ordinance.
For jobs in Los Angeles, please click “here” for information regarding the Los Angeles Fair Chance Initiative for Hiring Ordinance.
To view the “EEO Know Your Rights” poster click “here”. This poster provides information concerning the laws and procedures for filing complaints of violations of the laws with the Office of Federal Contract Compliance Programs.
To view the FMLA poster, click “here”. This poster summarizing the major provisions of the Family and Medical Leave Act (FMLA) and telling employees how to file a complaint.
It is the Company’s policy to employ the best qualified individuals available for all jobs. Therefore, any discriminatory action taken on account of an employee’s ancestry, age, color, disability, genetic information, gender, gender identity, gender expression, sexual and reproductive health decision, marital status, medical condition, military or veteran status, national origin, race (include traits historically associated with race, including, but not limited to, hair texture and protective hairstyles), religion (including religious dress), sex, or sexual orientation that adversely affects an employee's terms or conditions of employment is prohibited. This policy applies to all aspects of the employment relationship, including, but not limited to, hiring, training, salary administration, promotion, job assignment, benefits, discipline, and separation of employment.
Salary Context
This $134K-$209K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Allstate Insurance, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. Disclosed range: $134K to $209K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Allstate Insurance AI Hiring
Allstate Insurance has 13 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, AI Product Manager. Positions span Remote, US, Charlotte, NC, US. Compensation range: $66K - $209K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.