Interested in this AI/ML Engineer role at Batteries Plus?
Apply Now →About This Role
The AI Product Owner partners with business stakeholders, Technical Delivery Managers, engineering teams, and AI solution providers to deliver AI\-enabled capabilities that drive business value across Batteries Plus. This role is responsible for understanding business needs, prioritizing AI opportunities, aligning stakeholders, and supporting the successful implementation and adoption of AI solutions.
Essential Duties and Responsibilities
Product Ownership \& Business Alignment
- Partner with business stakeholders to support the implementation of the enterprise AI roadmap and identify opportunities to improve customer experience, employee productivity, operational efficiency, and decision\-making.
- Define and prioritize business requirements, product enhancements, and AI use cases based on business value and organizational objectives.
- Serve as the voice of the business, ensuring stakeholder needs, user expectations, and success criteria are represented throughout the product life cycle.
- Provide product guidance and prioritization to support the successful delivery and adoption of AI\-enabled solutions.
Cross\-Functional Collaboration
- Act as a liaison between business teams, Technical Delivery Managers, engineering teams, data and analytics partners, and external vendors.
- Facilitate discovery sessions, workshops, and stakeholder reviews to gather requirements, align priorities, and support decision\-making.
- Collaborate with delivery teams to ensure solutions meet business objectives and operational needs.
- Support identification and resolution of business\-related risks, dependencies, and implementation challenges.
AI Solution Enablement \& Adoption
- Evaluate and prioritize AI use cases based on business value, feasibility, and user impact.
- Partner with technical teams to understand AI capabilities, limitations, and implementation considerations.
- Ensure AI solutions align with established governance, security, compliance, and responsible AI practices.
- Support adoption planning, user engagement, and operational readiness activities.
- Gather user feedback and monitor adoption, utilization, and business outcomes to drive continuous improvement and value realization.
Value Realization \& Reporting
- Drive adoption and business value realization of AI\-enabled solutions.
- Track, measure, and report on adoption, performance, and business outcomes.
- Identify and support opportunities for continuous improvement and operational impact.
- Develop and deliver concise presentations and communications for executive, technical, and franchise audiences.
- Success in this role will be measured by stakeholder adoption of AI\-enabled solutions, use cases prioritized and delivered against the enterprise AI roadmap, and measurable business outcomes such as productivity gains, process efficiency, or improved customer and employee experience.
Other Duties
This job description is intended to outline the general nature and key responsibilities of the role. It is not intended to be an exhaustive list of all duties, responsibilities, or qualifications associated with the position. Duties and responsibilities may evolve or change over time based on business needs, without advance notice.
Why Work With Us At Batteries Plus, we aren't just keeping up with the future of retail and technology\-we are actively building it. We are deeply committed to practical, cutting\-edge innovation, having already integrated advanced artificial intelligence and smart automation across our business to elevate everything from our commercial sales outreach to intelligent voice systems supporting our 800\+ locations. Instead of just talking about the future of tech, we are actively implementing it to solve real\-world challenges and streamline how we operate. Joining us in this position means stepping into a collaborative, forward\-thinking environment where your ideas turn into high\-visibility solutions. If you are eager to learn, grow, and lead the charge in meaningful digital transformation alongside a team that values continuous improvement, Batteries Plus is where you can truly charge your career forward. Education and Experience
- Bachelor's Degree in Business, Marketing, Information Technology, or a related field required.
- 5\+ years of experience as a Product Owner, Product Manager, Business Analyst, or related role.
- Experience delivering technology, digital transformation, automation, analytics, or AI\-related initiatives.
- Strong understanding of Agile delivery methodologies and product management practices.
- Exceptional stakeholder management and cross\-functional collaboration skills.
- Ability to translate business needs into actionable requirements and business outcomes.
- Strong analytical, organizational, and problem\-solving skills.
- Excellent written, verbal, and presentation skills, capable of communicating clearly to both executive and technical audiences.
- Demonstrated hands\-on experience working with AI\-powered products, including but not limited to intelligent assistants, workflow automation, or AI\-enabled search, with a track record of translating those capabilities into adopted, business\-facing solutions.
- Demonstrated ability to work directly with business teams to identify opportunities, redesign processes, and implement technology solutions that deliver measurable business outcomes.
- Experience operating in fast\-paced environments where ambiguity, experimentation, and cross\-functional collaboration are critical to success.
Physical Requirements / Work Environment
*The physical demands required to perform the essential responsibilities of this position are as follows. Reasonable accommodations, if necessary and/or as required by law, will be made available.*
- Regularly required to sit, use hands, talk, and hear.
- Occasionally required to stand, walk, reach overhead, and lift up to 20 pounds.
- Requires close vision and regular use of a computer and office equipment.
- This role operates in a standard office environment.
EEOC Statement
Batteries Plus is an Equal Opportunity Employer. Applicants and associates are free from discrimination on the basis of race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), parental status, national origin, age, disability, genetic information (including family medical history), political affiliation, military service, pregnancy accommodations, reprisal, other non\-merit based factors, and any other protections afforded under state or local laws.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Batteries Plus, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Batteries Plus AI Hiring
Batteries Plus has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Hartland, WI, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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