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About This Role
About Us
============
### We’re not like other insurance companies. From our specialty products to our business model, our culture to our results — we’re different. Different is who we are, and how we work, interact, deliver and succeed together. Creating a different and better insurance experience doesn’t just happen. It takes focus and a shared passion for going beyond the expected to forge relationships and deliver care that makes a difference. This approach rises from and is supported by our talented, ethical and smart team of employee owners united around a single purpose: to work alongside our customers and partners when they need us, in unexpected ways, with exceptional results. Apply today to make a difference with us.
### RLI is a Glassdoor Best Places to Workcompany with a strong, successful background. For decades, our financial track record has been stellar — a testament to our culture and validation of our reputation as an excellent underwriting company.
Position Purpose
====================
Under general management, design, develop, and deliver next\-generation AI solutions that improve business processes across the enterprise. Develop intelligent applications, integrate AI capabilities with enterprise systems, and build scalable cloud\-native solutions that enable automation, knowledge discovery, and operational efficiency. Collaborate with product owners, architects, business stakeholders, and development teams to design secure, scalable, and maintainable AI\-enabled solutions that solve complex business challenges.
Principal Duties \& Responsibilities
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- Design, develop, test, and deploy enterprise AI applications using modern software engineering practices.
- Build intelligent solutions utilizing Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), AI agents, and workflow orchestration technologies.
- Develop scalable backend services, REST APIs, and cloud\-native applications that integrate AI capabilities with enterprise systems.
- Design and implement integrations using APIs, messaging, and event\-driven architectures.
- Develop document processing, OCR, and knowledge extraction solutions using AI technologies.
- Design and implement vector search, semantic retrieval, and enterprise knowledge base solutions.
- Deploy, manage, and optimize applications within Kubernetes\-based environments.
- Collaborate with business partners to understand business needs and translate them into practical AI\-enabled solutions.
- Participate in architecture reviews, code reviews, Agile ceremonies, technical planning, and solution design discussions.
- Contribute to CI/CD pipelines, infrastructure automation, and software engineering best practices.
- Mentor team members and promote high standards for software quality, testing, security, observability, and maintainability.
- Assist with special projects or perform other duties as assigned.
Education \& Experience
===========================
- Typically requires a Bachelor’s degree in computer science or a related field
- Minimum of 7 years of professional software engineering experience designing, developing, and supporting enterprise applications.
+ Equivalent combination of education and experience will be considered.
- Experience designing and developing cloud\-native applications, distributed systems, and RESTful APIs.
- Experience developing AI\-enabled applications utilizing Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), AI agent frameworks, and prompt engineering techniques.
- Experience designing and implementing vector databases, vector search, embeddings, and semantic retrieval solutions.
- Experience using AI\-assisted software development tools to improve software design, development, testing, and delivery.
- Experience working within Agile software development environments utilizing Git\-based source control and CI/CD practices.
Knowledge, Skills, \& Competencies
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- Strong proficiency in Python and modern software engineering principles.
- Strong understanding of software architecture, object\-oriented design, and design patterns.
- Ability to design and develop scalable cloud\-native applications utilizing Kubernetes, Docker, REST APIs, and microservices.
- Knowledge of modern AI application architecture including Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), AI agents, LangGraph, LangChain, Azure AI Foundry, prompt engineering, and AI model evaluation.
- Ability to design and implement vector search, semantic retrieval, embeddings, and knowledge ingestion pipelines for enterprise AI solutions.
- Knowledge of intelligent document processing technologies including OCR, document processing pipelines, and structured and unstructured document extraction.
- Experience integrating enterprise systems using APIs, messaging, and event\-driven architectures.
- Knowledge of DevOps tools and practices including Git, GitLab, GitLab CI/CD, Infrastructure as Code (IaC), and automated deployment pipelines.
- Ability to leverage AI\-assisted development tools while maintaining high standards for software quality, security, testing, and observability.
- Strong analytical, problem\-solving, communication, and collaboration skills.
- Demonstrates the ability to evaluate emerging AI technologies and apply them to solve complex business problems.
Compensation Overview
=========================
### The base salary range for the position is listed below. Please note that the base salary is only one component of our robust total rewards package at RLI. The salary offered will take into account a number of factors including, but not limited to, geographic location, experience, scope \& responsibilities of the role, qualifications/credentials, talent availability \& specialization, as well as business needs. The below range may be modified in the future.
Base Pay Range
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$120,515\.00 \- $175,650\.00Total Rewards
=================
### At RLI, we're all owners. We hire the best and the brightest employees and allow them to share in the company's success through our Total Rewards. With the Employee Stock Ownership plan at its core, the Total Rewards program includes all compensation, benefits and perks that come with being an RLI employee.
Financial Incentives
------------------------
- ### Annual bonus plans
- ### Employee stock ownership plan (ESOP)
- ### 401(k) — automatic 3% company contribution
- ### Annual 401k and ESOP profit\-sharing contributions (Up to 15% of eligible earnings)
Work \& Life
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- ### Paid time off (PTO) and holidays
- ### Paid volunteer time off (VTO) to support our communities
- ### Parental and family care leave
- ### Flexible \& hybrid work arrangements
- ### Fitness center discounts and free virtual fitness platform
- ### Employee assistance program
Health \& Wellness
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- ### Comprehensive medical, dental and vision benefits
- ### Flexible spending and health savings accounts
- ### 2x base salary for group life and AD\&D insurance
- ### Voluntary life, critical illness, \& accident insurance for purchase
- ### Short\-term and long\-term disability benefits
Personal \& Professional Growth
===================================
### RLI encourages its employees to pursue professional development work in insurance and job\-related areas. We make a commitment to employees to provide educational opportunities that help them enhance their skills and further their career advancement. RLI fosters a true learning culture and encourages professional growth through insurance courses, in\-house training and other educational programs. RLI covers the cost for most programs and employees typically earn a bonus upon successful completion of approved courses and certifications. Our personal and professional growth benefits include:
- ### Training \& certification opportunities
- ### Tuition reimbursement
- ### Education bonuses
Diversity \& Inclusion
==========================
### Our goal is to attract, develop and retain the best employee talent from diverse backgrounds while promoting an environment where all viewpoints are valued and individuals feel respected, are treated fairly, and have an opportunity to excel in their chosen careers. We actively support, and participate in, initiatives led by the American Property Casualty Insurance Association that aim to increase diversity in the insurance industry. Cultivating an exceptional and diverse workforce to deliver excellent customer service reinforces our culture and is a key to achieving superior business results.
### RLI is an equal opportunity employer and does not discriminate in hiring or employment on the basis of race, color, religion, national origin, citizenship, gender, marital status, sexual orientation, age, disability, veteran status, or any other characteristic protected by federal, state, or local law.
Salary Context
This $120K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At RLI, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($148K) sits 31% below the category median. Disclosed range: $120K to $175K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
RLI AI Hiring
RLI has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $175K - $203K.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% below the national median.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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