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About This Role
ERP Suites is dedicated to delivering innovative technology solutions that help organizations streamline operations, improve productivity, and accelerate growth. We are seeking an AI \& Mobile Product Manager to lead the strategy, development, and delivery of next\-generation AI\-powered and mobile solutions that create measurable business value for our customers.
Location:
Our home office is based in Cincinnati, OH. However, we are open to hiring someone who is fully remote regardless of location. Although the position will be remote, there might be some occasional travel to ERP Suites facilities or customer sites.
Position Summary:
The AI \& Mobile Product Manager will own the product lifecycle from concept through adoption, driving the development of AI and mobile solutions that solve meaningful business challenges. This role serves as the bridge between customers, business stakeholders, engineering teams, data scientists, and leadership to ensure products are valuable, scalable, secure, and aligned with business objectives.
The ideal candidate combines strong product management expertise with a practical understanding of artificial intelligence, including generative AI, machine learning, AI agents, automation, model governance, and responsible AI practices.
Daily Activities:
- Collaborate with engineering, data science, and design teams to review progress and resolve blockers.
- Refine requirements, user stories, and product priorities.
- Review customer feedback, support issues, usage analytics, and product performance metrics.
- Participate in Agile ceremonies including sprint planning, backlog grooming, standups, and demos.
- Review workflows, wireframes, prompts, model behavior, and AI\-generated recommendations.
- Support sales, marketing, and customer success teams with product knowledge and enablement.
Key Responsibilities:
Product Strategy \& Roadmap:
- Define and execute the AI product vision, strategy, and roadmap.
- Identify and prioritize AI and mobile use cases based on customer value, business impact, feasibility, and risk.
- Develop business cases and product strategies that align with organizational goals.
- Conduct market research, competitive analysis, and customer discovery to identify opportunities.
Product Development \& Delivery:
- Translate business requirements into detailed product requirements, user stories, and acceptance criteria.
- Partner with engineering, architecture, UX, and data science teams to design scalable solutions.
- Manage product backlogs, sprint planning, prioritization, and release planning.
- Ensure successful delivery through Agile product development methodologies.
- Oversee product releases, documentation, training, and readiness activities.
Customer Experience \& Adoption:
- Collaborate with UX teams to create intuitive, AI\-enabled user experiences.
- Gather and analyze customer feedback to continuously improve products.
- Define success metrics, KPIs, adoption goals, and ROI measurements.
- Monitor product performance, user adoption, customer satisfaction, and AI model effectiveness.
Governance \& Risk Management:
- Ensure AI products are secure, explainable, compliant, and aligned with responsible AI principles.
- Collaborate with security, legal, and compliance teams to manage AI\-related risks.
- Evaluate AI outputs for accuracy, usability, trustworthiness, and business relevance.
Cross\-Functional Leadership:
- Partner with stakeholders across product, engineering, sales, marketing, customer success, and executive leadership.
- Communicate product strategy, priorities, trade\-offs, and roadmap updates.
- Support go\-to\-market initiatives, sales enablement, product positioning, and customer adoption.
Qualifications:
Required:
- Bachelor's degree in Business, Computer Science, Information Systems, Engineering, Data Science, or a related field.
- 3\+ years of experience in product management, business analysis, software delivery, or technology strategy.
- Experience delivering software products, digital platforms, enterprise applications, or data\-driven solutions.
- Working knowledge of AI concepts including machine learning, generative AI, natural language processing, predictive analytics, and AI agents.
- Experience collaborating with technical teams such as software engineers, architects, and data scientists.
- Strong experience writing product requirements, user stories, acceptance criteria, and business cases.
- Experience using Agile methodologies and product management tools.
- Excellent communication and stakeholder management skills.
- Strong analytical, problem\-solving, and decision\-making abilities.
Core Competencies:
- Product strategy and roadmap development
- AI, machine learning, generative AI, and automation concepts
- Customer discovery and use case validation
- Agile product management and backlog prioritization
- Data\-driven decision making and KPI development
- Cross\-functional leadership
- UX and workflow design
- Executive communication and presentation skills
- Risk management, governance, and responsible AI practices
Preferred Qualifications:
- Experience managing AI, machine learning, generative AI, or automation products.
- Experience with enterprise systems such as ERP, CRM, HCM, supply chain, finance, or operations platforms.
- Familiarity with cloud AI platforms including Oracle Cloud Infrastructure, Microsoft Azure AI, AWS AI, Google Cloud AI, or OpenAI technologies.
- Knowledge of AI governance, model monitoring, prompt engineering, data privacy, security, and compliance.
- Understanding of APIs, data pipelines, vector databases, retrieval\-augmented generation (RAG), large language models (LLMs), and agentic AI architectures.
- Experience launching AI products in enterprise or regulated industries.
- Experience developing ROI models and business value frameworks for AI initiatives.
- Product certifications such as Certified Scrum Product Owner (CSPO), SAFe Product Owner/Product Manager, or Pragmatic Institute certification.
Company:
At ERP Suites, our focus is on helping our customers realize IT’s potential. Our comprehensive ERP solutions enable them to streamline and scale their IT products and processes. And this leads directly to improved efficiency and increased margins. We are a proud Oracle Gold Partner and champion of proactive JD Edwards management and custom product enhancements.
ERP Suites provides technical consulting, cloud services, managed services, and digital transformation solutions for some of America’s top companies. We build secure connections, improve performance, automate workloads and give them mobility. In other words, we help them stay on top.
We deliver multi\-functional value through cloud services, digital transformation, ERP consulting services, ERP managed services, and software development.
Core Values:* Make Customers Successful
- Be An Advisor
- Be a teacher
- Be a Coach
- Have Fun
- Do the Right Things for the ERP Suites Family
- Adapt Quickly to Changing Roles and Environments
This is Where IT Change Starts.* With questions.
- With problems that need to be solved.
- With business needs, both immediate and long term.
- Because technology and its impact on business isn’t getting any simpler.
- That’s why we exist.
- To answer the tough questions.
- To find a solution to every problem—no matter the size or scope.
- And to help companies not just identify IT’s potential, but realize IT
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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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At ERP Suites, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
ERP Suites AI Hiring
ERP Suites has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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