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About This Role
Company Description
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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 the acquisition of *Kavida.ai*, *QAD is fast\-tracking its Champion AI roadmap* — agentic AI “digital workers” that automate critical procurement and supply\-chain workflows and free up to 50% of a buyer’s workday.
Job Description
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Champion AI is the strategic future of QAD \| Redzone, and we are building a dedicated AI division to own it — this is where the company’s attention, investment, and ambition are concentrated. The strategy and the technical foundation are built; we need solution designers who can execute it and scale it across customers.
There has never been a better moment to sit at the frontier of agentic AI for manufacturing. We are assembling a small team of operators to capture it, and this role helps define what “great” looks like.
The Role
The AI Sales Solutions Engineer is a solution designer. You walk into a customer conversation, understand their end\-to\-end process, and assemble an agentic solution to fix it — without engineering involvement.
You sit on top of a pre\-built foundation — data, semantics, an agent platform, and a library of persona agents. You configure within it; you don’t build it. The platform exposes configuration through natural language and drag\-and\-drop, so your edge is domain fluency and solution\-design instinct, not code.
What You’ll Do
- Working hand\-in\-hand with the Account Executive, you take a qualified opportunity and turn it into a working agentic solution:
- Run process discovery workshops: sit with champion users to map any of the standard enterprise processes (P2P, S2C, O2C, P2M, F2P, R2R) — current state, KPIs, manual effort, bottlenecks, and target state.
- Design the agentic solution: decide which agents, working together, fix the customer’s process — and recognize whether it’s a greenfield build or a brownfield plug\-in to an existing system (SAP, Ariba, Coupa, Jaggaer).
- Map KPIs to the platform: confirm the system has the data and the business context to measure what the customer actually cares about.
- Configure each agent: set triggers, decision logic, exceptions, KPIs, data sources, communications, company\-specific rules, and hand\-offs — all in natural language and drag\-and\-drop, no code.
- Present the solution: pitch anchored in business outcomes — OTIF, working capital, cycle time, cost reduction — worked backwards to the agents that achieve them.
Qualifications
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- Enterprise domain fluency: operational, end\-to\-end working knowledge of at least two of P2P, S2C, O2C, P2M, F2P, R2R — not just a consulting view. The deepest value sits in plan\-to\-make and procure\-to\-pay.
- Solution\-design thinking: ability to decompose a vague business outcome into agents and data flows, and to know when one agent suffices and when several are needed.
- Low\-code / natural\-language comfort: hands\-on with tools like n8n, Zapier, or Make. The agent platform will feel familiar.
- Customer\-facing credibility: workshop facilitation and executive\-level presentation.
Nice\-to\-Have
- Background at a major ERP vendor or process consultancy (SAP, Oracle, Coupa, Ariba, or a Big Four firm).
- Hands\-on experience with AI tooling.
- Industry depth in manufacturing, particularly tier\-one suppliers.
*Not Required*
*Model\-building, software\-engineering depth, or an ML / data\-science background. Engineering owns the platform; you own the solution.*
Who Thrives Here
- You are joining a small, entrepreneurial team — not a delivery machine with a ticket queue. We want self\-starters who see the opportunity in agentic AI and move without waiting for instructions.
- Self\-starter: if a solution pattern or workshop framework doesn’t exist yet, you create it. If a document is missing, you write it.
- Resourceful: you pull in the right people — your AE, Product, and Customer Success — to move a deal forward and get the customer to value.
- Builder’s mindset: you take a strong foundation and a playbook and extend them as the platform matures, rather than waiting for every process to be defined for you.
- If you need rigid structure and a clearly defined process for every situation, this is not the right place. If you want to shape how this role and this division are built, it is.
Additional Information
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- Compensation. $100,000— 140,000 USD ,
- Plus a strong benefits package inclusive of PTO, 401K matching, and Healthcare/Medical Insurance plans.
- Virtual\-First Culture: work remotely, with the flexibility to lead in\-person customer workshops in your market.
- Atmosphere of Growth: join a team where idea\-sharing is prioritized over hierarchy, at the leading edge of agentic AI for manufacturing.
- Comprehensive Benefits: health, dental, vision, PTO, 401K matching, and a collaborative culture of smart, hard\-working people.
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.
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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 QAD, 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.
QAD AI Hiring
QAD has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, 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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