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About This Role
Location Schaumburg, United States Job type Full\-Time Work mode Hybrid Job level Management \& Leadership Job ID 12655 Company Roanoke Insurance Group Inc Employment type Regular Area of expertise Claims \& Underwriting
Location:
- Schaumberg, IL Hybrid Role: In\-office two days of your choice. Work remotely three days a week.
Job Description
We are in search of an experienced insurance professional responsible for investigating, evaluating, and settling complex cargo damage claims by examining policies, gathering evidence, analyzing loss details, negotiating settlements with involved parties, and ensuring compliance with company guidelines, often handling high\-value or intricate claims with a focus on minimizing losses; requiring strong knowledge of cargo insurance policies, shipping regulations, and claims handling procedures.
The Complex Claim Manager will ensure all claims are handled in a timely basis. Performs administrative functions related to the handling of those claims, and will follow the Claims Departments Policies, Procedures, Rules and Regulations. Act as senior investigator, managing litigation strategies, communicating with stakeholders, and providing technical expertise on complex cases.
This role reports to the SVP Claims.
Responsibilities
- Follow the day\-to\-day operations of the claims department and ensure that claims are processed within client and company guidelines
- Claim Investigation \- Thoroughly reviewing claim documents, including bills of lading, shipping manifests, inspection reports, and policy details to determine coverage and potential liability. Interviewing relevant parties like shippers, consignees, carriers, and witnesses to gather information regarding the incident. Analyzing loss reports and damage appraisals to verify claim validity.
- Loss Evaluation \- Determining the cause of loss and assigning responsibility based on policy terms and industry standards. Calculating the total claim amount considering repair costs, replacement value, and potential salvage value. Identifying potential subrogation opportunities to recover losses from liable third parties.
- Claim Resolution \- Negotiating settlement amounts with claimants, brokers, and legal counsel to reach a fair and timely resolution. Authorizing claim payments within established limits and company guidelines. Preparing detailed claim reports and documentation to support settlement decisions.
- Complex Claim Management \- Leading the handling of large or complex cargo claims involving multiple parties, legal disputes, or significant financial exposure. Collaborating with internal and external experts, including surveyors, appraisers, and legal counsel, to resolve intricate claim issues.
- Customer Service \- Providing timely updates and clear communication to all parties involved throughout the claims process. Managing customer expectations and addressing concerns regarding claim handling.
- Collaborate with the co\-workers as well as leadership from cross functional departments to ensure efficiency and proper prioritization of the claims process
- Updates records, files and computer databases as needed; ensuring all information is current and up to date per company guidelines.
- Provide assistance to departmental management for projects as needed
- Assists in special project needs as they relate to claims services.
- Interact professionally with clients, team members and other departments
- Collaborate with and openly share knowledge with colleagues
- Serves as point of contact and liaison within the Claims teams supporting both binder and other market claims/customers
- Works with Compliance to prepare and respond to Department of Insurance Complaints, BBB complaints, as well as taking calls from customers with questions and/or complaints when applicable.
- Handle all payments, claim closing and reserve in accordance to the company policy and procedures, ensuring all are current and correct.
- Have ability to change priorities depending on the inventory load, handling claims
- Evaluates and maintains all documents/information received, and ensures all information is submitted to the appropriate insurer if applicable in an expedient manner.
Requirements
- 5\-10\+ years of experience in cargo insurance claims handling, preferably with a focus on complex marine claims
- Strong understanding of:
+ Insurance policy language
+ Shipping regulations
+ Industry practices
- Analytical and problem\-solving skills to assess complex claims and determine liability
- Excellent negotiation and communication skills to interact with diverse stakeholders
- Ability to work independently and manage a large caseload effectively
- Strong attention to detail, written communication and organizational skills
- Specialized industry knowledge, including complex shippers' interest claims
- Experience with claims adjudication, systems, and reporting
- Solid writing skills to create accurate claim data
- Specialized industry knowledge, including complex shippers' interest claims
- Experience with claims adjudication, systems, and reporting
Authority Levels, Education, Certificates, Licenses and Designations
- *Required: All applicable State licenses and designations*
People Leadership
- This role *will* have people leadership responsibilities at this time
We pride ourselves on having a diverse workforce. We value and celebrate the uniqueness of individuals and the different perspectives they provide. We offer equal opportunity employment regardless of race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability status, age, marital status, or protected veteran status.
Your Benefits
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With us, you get more than just an exciting job. Enjoy a wide range of employee benefits tailored to your wellbeing and development. Please note that regional differences may apply.
Competitive Salary
We provide fair and competitive compensation that reflects your performance and commitment.
Company performance\-based Incentives
In addition to your salary, our variable compensation approach allows you to share in Munich Re's success.
Recognition and Special Rewards
We recognise outstanding individual contributions through a variety of targeted rewards and incentives.
Retirement Planning
We support your long\-term financial wellbeing with retirement solutions aligned with local regulations.
Inclusive Workplace Culture
We foster a respectful, inclusive, and values\-driven environment.
Learning \& Development
We offer tailored learning opportunities with a strong focus on core skills and business\-critical knowledge.
Work\-Life\-Balance
Supporting your ability to balance family, leisure, and your career.
Health \& Wellbeing
We support your physical and mental health through a range of programs and activities in each location.
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 Roanoke Insurance Group, 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.
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.
Roanoke Insurance Group AI Hiring
Roanoke Insurance Group has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Schaumburg, IL, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 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
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