Interested in this AI/ML Engineer role at TireHub?
Apply Now →Skills & Technologies
About This Role
Product Owner, Business Intelligence, Automation and AI
Position Summary:
At TireHub we move more than tires – we move businesses forward, support communities, and help keep America rolling.And behind it all?Our people.We call them Hubbers – because they’re at the center of everything we do.From behind the wheel to the warehouse floor, from customer calls to corporate strategy, every Hubber plays a role in something bigger than themselves.And we show up every day ready to say yes – to challenges, to each other, and to getting it done right.
The Product Owner, Business Intelligence, Automation and AI plans and coordinates the development, enhancement, prioritization, and adoption of AI, automation, data warehouse, and Power BI reporting products with TireHub’s business partners. This position provides product leadership and subject matter expertise across intelligent automation, applied AI, enterprise data, analytics, and reporting solutions. The Product Owner serves as the primary liaison between business stakeholders, technical teams, vendors, and leadership to ensure product roadmaps, backlogs, and delivered solutions align with TireHub’s strategic priorities, operational needs, governance standards, and measurable business value.
This position reports to Director, Enterprise Applications.
The individual must exhibit the following core TireHub commitments:
- Approachable - If a company could smile, we would. Instead, we rely on our people to show it. We care about each other and our customers because we know business only gets done right when people respect each other and value relationships.
- Adventurous - What TireHub is set up to do is intentionally outrageous. So, we readily embrace challenges with the courage to introduce new ideas and the ambition to build something unique.
- Relentless - We tackle our work with energy. We deliver on our commitments with enthusiasm. And we don't give up until we get to the end.
- Speedy - Speed is the currency in the tire industry. When we commit to a job, we get the job done – and we do it fast.
Role Specifics:
- Owns, maintains, and communicates the short-term and long-term product roadmap for AI, automation, enterprise data, data warehouse, and Power BI reporting capabilities.
- Partners with business leaders and stakeholders to identify, evaluate, and prioritize high-value use cases for AI, workflow automation, data products, dashboards, analytics, and reporting solutions.
- Maintains, and accounts for product team progress to business stakeholders in bi-weekly prioritization.
- Works with assigned architect and business analyst on ongoing process of backlog grooming and elaboration to prepare actionable content for sprints.
- Works with technical leads in assigned areas to plan appropriate level of resource augmentation with offshore partners.
- Works with the scrum master on fine tuning the agile process for the product team through retrospectives and metric analysis.
- Creates, maintains, and prioritizes product backlog items, user stories, epics, acceptance criteria, and delivery priorities based on business value, urgency, customer needs, resource availability, and strategic alignment.
- Acts as the primary point of contact between business stakeholders, data teams, automation engineers, AI enablement teams, vendors, and IT leadership to translate business needs into clear, actionable product requirements.
- Guides Agile delivery teams through successful delivery of committed sprints, ensuring requirements are clearly understood, business priorities are aligned, and blockers are resolved quickly.
- Collaborates with AI and automation technical resources to define, evaluate, pilot, and scale solutions such as workflow automation, robotic process automation, AI agents, copilots, generative AI-enabled processes, and intelligent business workflows.
- Works with data engineering and BI teams to support the data warehouse lifecycle, including source data intake, data modeling, transformation requirements, data quality expectations, metadata standards, and reporting needs.
- Partners with Power BI developers and business stakeholders to define trusted datasets, dashboards, reports, KPIs, data definitions, report adoption plans, and actionable insights for leadership and operational teams.
- Supports data governance, AI governance, security, privacy, and access control requirements by ensuring solutions follow TireHub standards for responsible AI use, data protection, and approved business processes.
- Prepares business cases, value estimates, adoption plans, and success metrics for AI, automation, data warehouse, and reporting initiatives.
- Evaluates emerging AI, automation, data, and analytics tools and capabilities, coordinating pilots and proofs of concept to determine business value and implementation feasibility.
- Facilitates QA, UAT, and validation activities to ensure delivered solutions meet business requirements, acceptance criteria, data quality expectations, and reporting accuracy standards.
- Monitors product performance, adoption, user feedback, and operational outcomes to continuously improve AI, automation, data, and reporting products.
- Communicates product priorities, delivery progress, risks, dependencies, and value realization to stakeholders and leadership in clear business terms.
- Facilitates problem resolution and removes blockers that are preventing delivery progression or product adoption.
- Participates and maintains ongoing training requirements through in-class, in-person, or computer-based learning modules, as assigned.
- Ensures individual adherence to TireHub policies, procedures, and guidelines.
- Completes other tasks assigned by their supervisor or another member of Leadership, as requested.
Competencies:
- Product Vision & Strategy: Defines and communicates clear product direction, roadmaps, priorities, and value outcomes for AI, automation, data, and reporting capabilities.
- Innovation Orientation: Commits to creating and exploring new approaches, particularly at the intersection of AI, automation, and analytics, that enhance business performance.
- Data-Driven Decision Making: Uses analytics, KPIs, product metrics, business outcomes, and stakeholder feedback to guide product decisions and prioritization.
- Business & Financial Acumen: Understands ROI, cost savings, efficiency gains, risk reduction, adoption, and business value when evaluating product investments and delivery decisions.
- Optimizes Work Processes: Identifies and implements efficient processes, automation opportunities, and continuous improvement practices to improve business outcomes.
- Collaborates: Builds strong partnerships and works collaboratively with business stakeholders, IT teams, vendors, and leadership to achieve shared objectives.
- Agility and Adaptability: Thrives in an Agile environment and embraces new technologies, methods, and business priorities in a fast-moving AI, automation, and analytics landscape.
- Communication: Communicates technical concepts, data insights, product priorities, and delivery progress clearly to both technical and non-technical audiences.
Education and Experience:
- Bachelor's degree in Information Systems, Computer Science, Data Analytics, Business, Engineering, or a related field required; an equivalent combination of education and experience will be considered.
- 5+ years of experience in product ownership, business analysis, product management, data analytics, business intelligence, automation, enterprise applications, or a related technology delivery role.
- 2+ years of experience applying Agile project management methodologies, preferably using JIRA or a similar tool.
- Experience creating product roadmaps, managing backlogs, writing user stories, defining acceptance criteria, and supporting Agile and Scrum delivery teams.
- Experience working with AI, automation, workflow modernization, Power Platform, Copilot, generative AI, or related emerging technology capabilities preferred.
- Experience with data warehouse, data lake, Lakehouse, ETL/ELT, data modeling, data governance, or enterprise reporting concepts preferred.
- Experience with Power BI reporting, including dashboards, datasets, KPIs, data visualization, and report adoption preferred.
- Experience translating business requirements into technical specifications for data, reporting, automation, or AI-enabled solutions.
- Strong organizational skills and demonstrated ability to manage multiple initiatives and drive them to successful, on-schedule completion.
- Experience working and collaborating with remote or geographically distributed team members across multiple time zones.
- Experience in distribution, supply chain, retail, logistics, or a similar industry preferred.
Required Knowledge, Skills, and Abilities:
- Strong understanding of product ownership practices, including Agile delivery methodologies, backlog management, sprint planning, and stakeholder prioritization.
- Ability to analyze business processes, identify operational pain points, and translate improvement opportunities into clearly defined product requirements for AI, automation, data, and reporting solutions.
- Working knowledge of Power BI, including dashboard and report development, dataset management, KPI definition, data definitions, and visualization best practices.
- Working knowledge of data warehouse concepts, including data quality, data modeling, data integration, metadata management, and reporting governance.
- Familiarity with AI and automation concepts, including process automation, AI agents, copilots, generative AI applications, responsible AI principles, and human-in-the-loop review of AI-enabled outputs.
- Ability to partner effectively with technical teams across solution design, QA validation, user acceptance testing, implementation planning, and end-user adoption support.
- Strong coordination, triage, and prioritization skills, with the ability to manage multiple competing priorities in a fast-paced environment.
- Excellent written, verbal, and presentation communication skills, with the ability to convey complex technical concepts clearly to both technical and non-technical audiences.
- Demonstrated ability to influence and align cross-functional teams without formal authority.
- Ability to exercise sound, timely judgment and make value-driven decisions in ambiguous or rapidly evolving situations.
- Ability to remain calm, focused, and productive under pressure; adapts readily to changing priorities and drives constructive change when needed.
- Demonstrated commitment to organizational mission, vision, and values, with the ability to operate effectively within organizational structure, goals, and strategic objectives.
Working Conditions:
- This is a fast-paced and dynamic operating environment.
- Majority of time is spent sitting, constantly viewing monitors in a comfortable position with frequent opportunity to move around. There may be occasions to move or lift light articles.
- Must be able to work flexible hours to accommodate routinely critical system changes and emergency events.
- Must be able to travel up to 10%.
TireHub will consider the employment of all qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable state and local laws. TireHub maintains a drug-free workplace in accordance with state and federal law.
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At TireHub, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
TireHub AI Hiring
TireHub has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.