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About This Role
POSITION SUMMARY
The AI Business Analyst will be responsible for evaluating whether Allied’s internal AI solutions are functioning as intended, identifying where they succeed and where they fall short, and translating those findings into clear, actionable feedback for product and engineering teams.
ESSENTIAL FUNCTIONS
- Evaluate AI capabilities integrated into core systems (e.g., Dynamics CRM, claims adjudication platform, document repositories) to determine whether they are functioning as intended and delivering expected business outcomes
- Investigate edge cases, anomalies, and failure patterns by reviewing raw data, logs, and system outputs
- Collaborate with technical product owners and SMEs to document what is and isn’t working, and help translate findings into user stories and product requirements
- Participate in model and capability reviews, providing business-level validation of AI behavior and identifying gaps in functionality or alignment with intended use
- Contribute to the development of test scenarios and evaluation frameworks that reflect real-world usage and business expectations
- Recommend improvements or refinements to AI capabilities based on observed behavior, user feedback, and business context
- Support the AI Product Owner and QA team in defining acceptance criteria and success metrics for new AI features
- Help ensure that AI solutions are understandable, trustworthy, and aligned with Allied’s operational goals
- Other duties as assigned
EDUCATION
- Bachelor’s degree in related field or equivalent work experience required.
EXPERIENCE AND SKILLS
- 8–12 years of experience in business analysis, quality assurance, or systems evaluation roles, ideally within the insurance or administrative healthcare industry
- Deep subject matter expertise in claims, health call centers, provider operations, or health plan administration
- Strong analytical mindset with the ability to identify subtle patterns, inconsistencies, or edge cases in complex systems
- Comfortable working with structured and unstructured data, including reviewing logs, outputs, and raw files to trace issues or validate behavior
- Experience writing clear, structured documentation such as user stories, test cases, and issue reports
- Familiarity with AI/ML systems and their evaluation is a plus, but not required
- Excellent communication skills and the ability to collaborate across technical and non-technical teams
- Familiarity with tools for data inspection and analysis (e.g., Excel, SQL, Power BI)
- Experience with log analysis and debugging tools (e.g., Azure Monitor, Splunk)
- Exposure to API testing tools (e.g., Postman) and basic scripting or query languages is helpful
- Agile collaboration tools (e.g., Freshservice, Azure DevOps, or similar) for tracking issues and documenting requirements
- Play a key role in Allied’s AI delivery team, helping ensure that AI capabilities are not only technically sound but also aligned with business needs and user expectations
POSITION COMPETENCIES
- Accountability
- Analytical Thinking
- Collaboration
- Communication
- Customer Focus
- Functional Expertise
- Initiative
PHYSICAL DEMANDS
- This is a standard desk role requiring extended sitting and computer work.
WORK ENVIRONMENT
Remote
Here at Allied, we believe that great talent can thrive from anywhere. Our remote friendly culture offers flexibility and the comfort of working from home; while also ensuring you are set up for success. To support a smooth and efficient remote work experience, the internet connection must be obtained through a cable broadband or fiber optic internet service provider with speeds of at least 100Mbps download/25Mbps upload. Reliable internet service is essential for staying connected and productive.
The company has reviewed this job description to ensure that essential functions and basic duties have been included. It is not intended to be construed as an exhaustive list of all functions, responsibilities, skills, and abilities. Additional functions and requirements may be assigned by supervisors as deemed appropriate.
Compensation is not limited to base salary. Allied values our Total Rewards, and offers a competitive Benefit Package including, but not limited to, Medical, Dental, Vision, Life and Disability Insurance, Generous Paid Time Off, Tuition Reimbursement, EAP, and a Technology Stipend.
Allied reserves the right to amend, change, alter, and revise, pay ranges and benefits offerings at any time. All applicants acknowledge that by applying to the position you understand that the specific pay range is contingent upon meeting the qualification and requirements of the role, and for the successful completion of the interview selection and process. It is at the Company's discretion to determine what pay is provided to a candidate within the range associated with the role.
Salary Context
This $105K-$125K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Allied Benefit Systems, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($115K) sits 46% below the category median. Disclosed range: $105K to $125K.
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.
Allied Benefit Systems AI Hiring
Allied Benefit Systems has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $125K - $125K.
Location Context
AI roles in Chicago pay a median of $193,440 across 429 tracked positions. That's 9% 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 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).
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 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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