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About This Role
Title: International Mail Support Analyst
Location: Chicago, IL
Security Clearance: Moderate Background Investigation (MBI)
Schedule: Tuesday\-Saturday 10\-6:30PM
Due to the nature of law enforcement work and operation, position may require occasional support outside of core working hours, as well as intermittent weekend support, mission dependent.
Travel: This position will require travel between worksites in their personally owned automobile (mileage reimbursed).
Salary: $72,000/Annually
About KACE:
When you make the decision to join KACE, you are choosing to work alongside talented professionals that have one thing in common; the passion to make a difference! KACE employees bring their diverse talents and experiences to work on critical projects that help shape the nation’s safety, security, and quality of life. The desire to have a career that is purposeful and forward thinking is woven into every KACE employee…it’s The KACE Way. KACE employees are; purpose driven, forward focused, open\-minded, trustworthy and invested. The KACE Way is our commitment to our employees, to our customers, and to our communities. Join KACE and make a difference!
Job Summary:
The International Mail Support Analyst supports Inspection Service international mail control and security related programs, including, but not limited to, import/export controls and interdictions of foreign lotteries, counterfeit US Postal Money Orders, and mail containing short paid/invalid postage.
Essential Functions and Responsibilities:
- Ability to communicate orally and in writing is sufficient to express thoughts and ideas to a variety of people;
- Proficiency conducting research on the internet and commercial as well as public databases;
- Extensive experience performing appropriate analytical techniques and methods when conducting international mail control and security related activities;
- Demonstrated ability to work with minimal direct supervision; and maintain confidentiality of the work performed;
- Extensive experience supporting the preparation of a final work product.
- Conducts evaluations of Customs data to identify instances of non\-compliance.
- Conducts triage of mail pieces for violations of export control, foreign lottery, and USPS revenue products, e.g. online postage, dangerous goods and money orders; and handles mail pieces through processes for hold out, return to sender and seizure.
- Renders items to Program Managers and/or Postal Inspectors assigned to Global Security for criminal investigation and/or seizure.
- Conduct research, such as data\-mining Postal records to identify fraudulent activity and/or instances of non\-compliance; prepare reports documenting analytical results; disseminate research results to appropriate contact(s) in a timely manner.
- Complete assigned tasks to ensure adherence to required timeframes in support of criminal or civil investigation and export control issues, as appropriate; assist Global Security with developing intelligence for potential criminal investigations; prepare initial investigative research; compile basic background information to include, but not limited to, researching all pertinent records and other data.
- Responsible for documenting and reporting analytical results, creating reports that present clear and concise representations of the information analyzed; and disseminating data as required by the assigned task(s).
- Reports all instances of non\-compliance in a timely, accurate and complete manner.
- Moves cleared items to dispatch in a timely fashion.
Minimum Qualifications \& Skills:
An Associates Degree (or equivalent college credit) from an accredited college or university and/or a minimum of five years of work experience demonstrating the ability to review financial information, conduct analysis, conduct research, communicate effectively orally and in writing.
Clearance:
Applicants selected may be subject to a government background investigation and may be required to meet the following conditions of employment.
Security Requirements/Background Investigation Requirements:
- Must be a U.S Citizen or Legal Permanent Resident.
- Favorable credit check for all cleared positions
- Successfully passing a background investigation including drug screening.
Benefits:
- Medical, Dental, and Vision Benefits
- HSA \& FSA
- 401(K) Retirement Savings Plan
- Life and AD\&D Insurance
- Disability Insurance
- Pet Insurance
Physical Requirements/Working Conditions :
Individuals must be physically able to perform efficiently the duties of the position, which require arduous exertion involving prolonged standing, walking, bending and reaching, climbing ladders, and may involve the handling of heavy containers of mail and parcels weighing up to 70 pounds.
*This job description reflects management’s assignment of essential functions; it does not prescribe or restrict the tasks that may be assigned. Management may revise duties as necessary without updating this job description.*
### For more information about the company please visit our website at www.kacecompany.com
### KACE is an Equal Opportunity Employer and does not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, disability or any other federal, state or local protected class.
### KACE complies with federal and state disability laws and makes reasonable accommodations for applicants and employees with disabilities.
### If you require reasonable accommodation in completing this application, interviewing, completing any pre\-employment testing, or otherwise participating in the employee selection process, please direct your inquiries to recruiting@kacecompany.com .
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 KACE Company, 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.
KACE Company AI Hiring
KACE Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US.
Location Context
AI roles in Chicago pay a median of $202,350 across 310 tracked positions. That's 10% above 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 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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