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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
About Law & Regulations
Law & Regulations delivers expert legal services that protect Allstate and its customers while ensuring compliance with evolving regulatory and ethical standards. We provide comprehensive support across claims litigation, complex corporate transactions, and enterprise-wide compliance programs, enabling the business to confidently navigate today's legal landscape.
By leveraging innovative legal strategies, modern technology, and emerging AI capabilities, we promote seamless collaboration, proactive risk management, intelligent decision-making, and effective problem solving across the organization. We demonstrate our core values through integrity, accountability, collaboration, and excellence. We are trusted legal advisors who place the needs of our customers and business partners at the heart of everything we do. We value each other, seek feedback, and take meaningful action to continuously improve.
As our legal technology ecosystem evolves, we embrace Generative AI, intelligent automation, and data-driven innovation to enhance operational efficiency, knowledge management, and service delivery while maintaining the highest standards of governance, security, privacy, and responsible AI.
Our collective success is enabled by collaboration across all areas of responsibility within Allstate and by upholding the legal and ethical foundation that drives business performance.
What We Deliver
Exceptional Legal Support: Expert counsel and representation that safeguard Allstate's interests and support our customers.
Compliance & Ethical Governance: Oversight of enterprise-wide privacy, regulatory compliance, and ethics programs.
Innovative Legal Operations: Use of agile processes, Generative AI, and modern technologies to enhance operational efficiency, knowledge discovery, and service delivery.
Job Summary
The AI Software Engineer designs, develops, deploys, and supports secure AI-powered enterprise applications, intelligent automation solutions, and system integrations that enable critical business operations. Working across backend services, frontend experiences, cloud-native architectures, and AI engineering, this role delivers scalable, reliable solutions to complex business challenges.
The ideal candidate combines software engineering expertise with hands-on experience in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI frameworks, and cloud-native technologies. This role includes ownership of technical solutions, participation in architectural discussions, and collaboration with business, legal, and technology teams to deliver innovative AI-driven products while adhering to governance, compliance, privacy, and security standards. Key Responsibilities
- Design, develop, and maintain backend services and APIs using modern frameworks (e.g., Java Spring Boot, Node.js, or Python FastAPI).
- Integrate and deploy Generative AI models (e.g., OpenAI, Hugging Face, LangChain) into production environments.
- Design and implement solutions leveraging LLMs, Generative AI, Agentic AI, and Retrieval-Augmented Generation (RAG) architectures.
- Fine-tune and optimize LLMs for specific use cases.
- Collaborate with product managers, architects, and cross-functional teams to define technical solutions and deliver business outcomes.
- Integrate AI/ML services and APIs into enterprise applications while following engineering and security best practices.
- Develop intelligent automation solutions that improve productivity, user experience, and operational efficiency.
- Troubleshoot, optimize, and enhance application performance, scalability, and reliability.
- Contribute to technical documentation, architecture discussions, and continuous improvement initiatives.
- Deploy and manage applications on cloud platforms, preferably Microsoft Azure.
- Participate in Agile ceremonies, including daily standups, sprint planning, backlog refinement, and retrospectives.
- Actively contribute to software development efforts by writing, reviewing, and maintaining high-quality, scalable, and secure code.
Required Skills
- 3+ years of software development experience using Python and/ or Java Spring Boot.
- Experience working with Generative AI APIs and/or open-source models.
- Experience with LangChain, LLM orchestration, or RAG (Retrieval-Augmented Generation) pipelines.
- Familiarity with prompt engineering, embedding models, or vector databases.
- Strong understanding of RESTful APIs, microservices architecture, and asynchronous processing.
- Knowledge of web and API security frameworks.
- Experience with relational databases NoSQL databases (MongoDB, DocumentDB), vector databases, Kafka, caching, and Kubernetes.
- Strong communication, collaboration, and problem-solving skills.
- Ability to manage multiple priorities and contribute effectively within a cross-functional team.
Preferred Skills:
- Exposure to multi-modal AI systems and agentic AI frameworks.
- Exposure to MLOps and model deployment workflows.
- Experience with Docker, Kubernetes, and CI/CD pipelines.
- Experience with cloud platforms (Azure, AWS or GCP).
- Knowledge of frontend frameworks like React or Angular.
Experience
- 4 or more years of experience
Skills
Cloud Platforms: Microsoft Azure (Azure OpenAI Service, Azure AI Search, Azure Machine Learning, ADLS), AWS
Containerization & Orchestration: Kubernetes, Docker, AWS EKS, Linux
Programming: Java, Spring Boot, Python, JavaScript/TypeScript, React
DevOps & Automation: Jenkins CI/CD, MLOps, Pipeline Orchestration
Data & Databases: SQL Server, MongoDB, DocumentDB, Vector Databases
AI/ML & LLM Technologies: Azure OpenAI, LangChain, Semantic Kernel, Agentic AI Frameworks, LLMs, Prompt Engineering, RAG
Professional Skills: AI Application Development, Full Stack Development, Problem Solving, Collaboration, Agile Development, Communication
Skills
Cloud Computing, Computer Science, Continuous Integration Software, LLM Architecture, Software Implementations, Web DevelopmentCompensation
Compensation offered for this role is 90,700.00 - 153,925.00 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.
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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.
Allstate provides a comprehensive technology setup, including a laptop, monitors, headset, keyboard, and mouse. Employees eligible to work from home also receive a monthly connectivity reimbursement to help offset internet costs.
When working from home, you must have a dedicated, private workspace free from distractions, along with appropriate desk and seating. Reliable internet is required, with minimum speeds of 50 MB download and 5 MB upload.
To help create a more personal and engaging experience, we ask that you join with your camera on if your interview is virtual. Please also have a Valid Photo Identification available at the start of your interview. If you require any accommodations or have questions ahead of your interview please reach out to your recruiter.
Note: Internal candidates are only asked to join on video; a Valid Photo Identification is not needed for the interview.
Salary Context
This $90K-$153K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $192K across 198 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Allstate Insurance, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,601 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($122K) sits 44% below the category median. Disclosed range: $90K to $153K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Allstate Insurance AI Hiring
Allstate Insurance has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager. Based in Remote, US. Compensation range: $145K - $181K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
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