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About This Role
Build the Future of Enterprise AI
AI Solutions Engineer
Wood Dale, IL \| Hybrid
Videojet is building a new AI Solutions team focused on delivering real business outcomes, not experimenting with technology for its own sake.
As an AI Solutions Engineer, you'll help transform business problems into production\-ready AI solutions that improve customer experiences, increase efficiency, and support growth across the enterprise.
You'll partner with business leaders, product managers, cloud teams, data teams, and technology partners to design, build, integrate, deploy, and support AI\-powered solutions.
What You'll Do
- Design and deploy enterprise AI services and workflows.
- Build retrieval systems that work with complex enterprise data.
- Integrate AI capabilities with CRM, knowledge, data, and operational platforms.
- Evaluate build, buy, and hybrid solution approaches.
- Develop AI agents and multi\-step workflows.
- Deploy cloud infrastructure using Infrastructure as Code.
- Establish monitoring, evaluation, and governance practices.
- Support production operations, reliability, and continuous improvement.
What Success Looks Like
- AI solutions adopted by business users.
- Reliable production systems with measurable outcomes.
- Secure and governed enterprise integrations.
- Strong evaluation frameworks and release processes.
- Continuous improvements in quality, reliability, and cost efficiency.
What You'll Bring
- 5\+ years of software, cloud, platform, or data engineering experience.
- Applied AI experience delivering production solutions.
- Strong Python development skills.
- Azure and/or AWS cloud experience.
- Enterprise API and integration experience.
- Experience with AI workflows, orchestration frameworks, and retrieval systems.
- Ability to translate business challenges into technical solutions.
Why Join Us
- Help shape a newly created AI function.
- Direct executive visibility.
- Opportunity to influence how AI is deployed across the enterprise.
- Access to modern cloud and AI technologies.
- Meaningful impact through real business outcomes.
US ONLY:
The below range reflects the range of possible compensation for this role at the time of this posting. We may ultimately pay more or less than the posted range. This range may be modified in the future. An associate’s position within the salary range will be based on several factors, including, but not limited to, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, shift, travel requirements, sales or revenue\-based metrics, any collective bargaining agreements, and business or organizational needs.
The compensation range for this role is $100,000 \- $140,000 USD per year. This job is also eligible for Bonus Pay.
We offer a comprehensive package of benefits including paid time off, medical/dental/vision insurance and 401(k) to eligible employees.
Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole discretion unless and until paid and may be modified at the Company’s sole discretion, consistent with the law.
US residents: In any materials you submit, you may redact or remove age\-identifying information such as age, date of birth, or dates of school attendance or graduation. You will not be penalized for redacting or removing this information.
Veralto Corporation and all Veralto Companies are committed to equal opportunity regardless of race, color, national origin, religion, sex, age, marital status, disability, veteran status, sexual orientation, gender identity, or other characteristics protected by law. We value diversity and the existence of similarities and differences, both visible and not, found in our workforce, workplace and throughout the markets we serve. Our associates, customers and shareholders contribute unique and different perspectives as a result of these diverse attributes.
The EEO posters are available here.
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform crucial job functions, and to receive other benefits and privileges of employment. Please contact us at [email protected] to request accommodation.
Unsolicited Assistance
We do not accept unsolicited assistance from any headhunters or recruitment firms for any of our job openings. All resumes or profiles submitted by search firms to any employee at any of the Veralto companies, in any form without a valid, signed search agreement in place for the specific position, approved by Talent Acquisition, will be deemed the sole property of Veralto and its companies. No fee will be paid in the event the candidate is hired by Veralto and its companies because of the unsolicited referral.
Salary Context
This $100K-$140K range is in the lower quartile 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 Videojet Technologies, 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 ($120K) sits 44% below the category median. Disclosed range: $100K to $140K.
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.
Videojet Technologies AI Hiring
Videojet Technologies has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in Wood Dale, IL, US. Compensation range: $140K - $140K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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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