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About This Role
Company Description
QAD is building a world\-class SaaS company, providing enterprise software solutions globally. We are a virtual\-first company, enabling our team to work primarily from home, while fostering a collaborative culture focused on growth, innovation, and well\-being. We value diversity and strive for an inclusive environment where everyone feels empowered to contribute to our success.
Job Description
The AI Solutions Architect is a senior, customer\-facing role at the front of our delivery motion. You are the person who sits with a customer, leads an agentic workshop on their core manufacturing processes, designs what an agent can and should do, and architects the solution.
This is not a slideware role, nor is it a hand\-off role. You own the solution end\-to\-end—from the first workshop through to a live, adopted production deployment—designing it, prototyping it, building it collaboratively with our offshore FDE team, and standing next to it at go\-live.
Key Responsibilities:
- Run Agentic Process Workshops: Lead structured workshops with customer process owners across core manufacturing value chains (Procure\-to\-Pay, Order\-to\-Cash, Source\-to\-Contract, and Plan\-to\-Manufacture). Separate standard habits from true human\-in\-the\-loop requirements and identify decision points an agent can own.
- Adapt Requirements into Agentic Flows: Turn messy spreadsheets, email threads, and tribal knowledge into structured agentic flows. Determine what the standard Persona Agent covers versus what needs custom engineering, and produce defensible work estimates up front.
- Architect Integrated Solutions: Own the technical design of the deployment, including integration across ERP systems (QAD and non\-QAD), PLM, EDI, email, and sourcing tools (e.g., Ariba, Coupa). Perform light prototyping on\-site to de\-risk complex elements quickly.
- Build End\-to\-End with the FDE Team: Stay embedded with the offshore FDE engineering team through the entire build phase. Pair on hard technical problems, resolve ambiguity, adjust designs live, and maintain strong customer relationships through go\-live.
- Drive Go\-Live \& Continuous Feedback: Run UAT against pre\-defined evaluation metrics, measure the attributed value delta post\-launch, and capture reusable components/accelerators to speed up future customer builds.
Qualifications Must\-Have Qualifications:
- Strong working understanding of modern AI agents, including LLMs, tool/function calling, RAG, autonomy, and human\-in\-the\-loop design patterns.
- Proven experience leading customer\-facing workshops or discovery sessions with enterprise stakeholders (e.g., Heads of Procurement or Supply Chain).
- Demonstrated ability to architect integrated enterprise solutions (ERPs, enterprise data flows, and contextualizing AI with customer data).
- Hands\-on technical depth to prototype and pair credibly with software engineers (ability to read, shape, and unblock code).
- Working fluency in at least two major enterprise value chains: Procure\-to\-Pay (P2P), Order\-to\-Cash (O2C), Source\-to\-Contract (S2C), or Plan\-to\-Manufacture (P2M).
Strongly Preferred \& Nice\-to\-Have:
- Background in software architecture, forward\-deployed engineering, or implementation leadership (e.g., experience at firms like Palantir, Microsoft Frontier, OpenAI, Anthropic, or enterprise AI/ERP software vendors).
- Hands\-on experience building/deploying agentic systems (prompt engineering, orchestration, evals).
- Familiarity with AWS cloud deployment, APIs, and enterprise data integration patterns.
Additional Information Compensation Package:
- Base Salary Range: $150,000 \- $185,000 USD annually, bonus eligible.
- Placement within our pay range will vary based on knowledge, skills, experience, and market location variations as well as internal peer equity.
- U.S. benefits package includes medical, dental and vision coverage, a 401(k) plan with company match, short\-term and long\-term disability coverage, life insurance, paid\-time off, parental leave, and well\-being programs.
About QAD and QAD Redzone:
QAD Inc. is a leading provider of adaptive, cloud\-based enterprise software and services for global manufacturing companies. Global manufacturers face ever\-increasing disruption caused by technology\-driven innovation and changing consumer preferences. In order to survive and thrive, manufacturers must be able to innovate and change business models at unprecedented rates of speed. QAD calls these companies Adaptive Manufacturing Enterprises.
QAD Redzone helps to enable QAD’s vision for the Adaptive Enterprise. Labor productivity improvements directly impact efficiency. Productive and empowered employees increase the effective capacity of your plant and accelerate time to productivity for new employees giving manufacturers the agility to increase production beyond what was previously possible without having to invest in production equipment or new plants, and reduce the amount and impact of employee attrition. Empowered employees with a growth mindset take extreme ownership of challenges that impact their production goals, creating resilience in the face of disruption.
We are an Equal Opportunity Employer and do 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, and basis of disability or any other federal, state or local protected class.
\#LI\-Remote
About QAD:
QAD \| Redzone is redefining manufacturing and supply chains through its intelligent, adaptive platform that connects people, processes, and data into a single System of Action. With three core pillars — Redzone (frontline empowerment), Adaptive Applications (the intelligent backbone), and Champion AI (Agentic AI for manufacturing) — QAD \| Redzone helps manufacturers operate with Champion Pace, achieving measurable productivity, resilience, and growth in just 90 days.
QAD is committed to ensuring that every employee feels they work in an environment that values their contributions, respects their unique perspectives and provides opportunities for growth regardless of background. QAD’s DEI program is driving higher levels of diversity, equity and inclusion so that employees can bring their whole self to work.
We are an Equal Opportunity Employer and do 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, and basis of disability or any other federal, state or local protected class.
\#LI\-Remote
Salary Context
This $150K-$185K 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 QAD, Inc., 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 ($167K) sits 22% below the category median. Disclosed range: $150K to $185K.
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.
QAD, Inc. AI Hiring
QAD, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $185K - $185K.
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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