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About This Role
Innovate in Charlotte
Thank you for dedicating your time and talent to Lowe’s. We want to give you more opportunities to learn and grow, so if you find a position you’re interested in below, we encourage you to apply!
Your Impact
The Sr Product Manager owns the vision and strategy, roadmap, and feature definition for large cross-functional and/or highly complex products. Responsibilities include defining the roadmap as well as planning and managing products and product features through the product development process. This role manages the product backlogs for a product group while prioritizing work and making product-related decisions based on the needs and expectations of customers and stakeholders. Finally, this role identifies and manages projects with a high level of autonomy. The role manages multiple product designs at any given time while partnering closely with the business to understand pain points and develop the go-to-market strategy.
What You Will Do
- Drives the vision and direction for the product, with support from product leaders. Finalizes the directional path by focusing on product value delivered in line with objectives and key results (OKRs). Drives product execution through a proactive cross-functional approach with relevant teams.
- Works through ambiguity to build OKRs for products that support product portfolio and organizational OKRs and aligns with business partners.
- Works with other product managers and business leaders to understand trade-offs and prioritization of features based on overall product vision. Establishes feature design trade-offs to drive critical/mass adoption.
- Defines customer/user experience that have impact to products at the domain level.
- Understands competitive trends within given product. Identifies opportunities to drive innovative customer experiences.
- Leads agile ceremonies and works with the team to clarify expectations and enables the scrum team to continually learn and improve.
- Engages teams to ensure usable, viable, and feasible products. Prioritizes effectively and communicates how user value is being delivered according to the product roadmap.
- Establishes product advocacy and builds influence amongst the product team, business partners, and directors.
- Serves as an expert in their product. Influences the direction of a well-defined product. Participates in aligning necessary work from partners to achieve the value of their product.
- Participates in product marketing and uses it to drive deep understanding of the assigned product with peers and leaders. Provides input to develop product market-fit strategies.
- Owns and drives product plans for a defined scope of the product. Leverages data to help drive alignment.
- Defines objectives and data required for analytics needed either independently or in conjunction with business partners for their product which is well defined. Leverages product analytics and insights.
- Defines key metrics of the product and consistently monitors health and business impact of the product. Partners with engineering to build tracking/monitoring needed to get to the data.
Required Qualifications
- Experience building, launching, or managing AI-powered products, agents, copilots, intelligent automation, or Generative AI solutions in an enterprise environment.
- Bachelor’s degree in business, marketing, engineering, communications, or related field or equivalent years of experience in lieu of education requirement, if applicable
- 5 years of experience in two or more of the following: project management, product management, business analysis, program management, experimentation, or product marketing
- 3 years of experience in product and/or experience management
- Experience in an agile software environment with strong writing ability
- Experience working cross-functionally in a large organization with senior leadership
- Experience working closely with senior leadership
- Experience translating data into quantifiable actions/deliverables
Preferred Qualifications
- 3+ years of experience with AI-powered products, platforms, agents, copilots, or intelligent automation solutions.
- Demonstrated experience translating business problems into AI and agentic solutions, including defining automation, human-in-the-loop controls, workflow orchestration, and measurable outcomes.
- Experience designing or product-managing agentic workflows, including agent intent, planning, tool calling, multi-step task execution, context/memory, routing, handoffs, escalation, and exception handling.
- Experience with Generative AI and Large Language Model (LLM)-based applications, including system prompt design, retrieval-augmented generation (RAG), response evaluation, guardrails, and iterative quality improvement.
- Experience defining AI evaluation frameworks and success metrics covering accuracy, task completion, reliability, adoption, user experience, cost, and business impact.
- Experience partnering with Engineering and Architecture on APIs, integrations, identity/authentication, enterprise data access, and orchestration layers that enable agents to take action across systems.
- Working knowledge of Model Context Protocol (MCP), Agent-to-Agent (A2A) patterns, or similar agent interoperability approaches.
- Experience with agent development platforms, low-code/no-code orchestration platforms, or agent development frameworks such as UnifyApps or similar technologies.
- Experience taking AI solutions from discovery and prototype through pilot, production launch, and ongoing optimization.
- Experience evaluating AI solutions for security, privacy, compliance, bias/fairness, explainability, auditability, and enterprise readiness.
- Strong ability to redesign end-to-end workflows around AI, rather than simply automate existing processes, including determining what should be handled by agents versus humans.
- Experience working across Product, Engineering, Architecture, Security, Data, Legal, Risk, and business teams to deliver enterprise AI solutions.
- Strong ability to drive outcome-based AI product management, with measurable business value, adoption, productivity, quality, and user experience improvements.
About Lowe’s
Lowe’s Companies, Inc. (NYSE: LOW) is a FORTUNE® 100 home improvement company with total fiscal year 2025 sales of more than $86 billion. Lowe’s employs approximately 300,000 associates and operates over 1,750 home improvement stores, 540 branches and 120 distribution centers. Lowe’s is a core value S&P 500 equity stock and a dividend aristocrat. Based in Mooresville, N.C., Lowe’s supports the communities it serves through programs focused on creating safe, affordable housing, improving community spaces, helping to develop the next generation of skilled trade experts and providing disaster relief to communities in need. For more information, visit Lowes.com.
Lowe’s is an equal opportunity employer and administers all personnel practices without regard to race, color, religious creed, sex, gender, age, ancestry, national origin, mental or physical disability or medical condition, sexual orientation, gender identity or expression, marital status, military or veteran status, genetic information, or any other category protected under federal, state, or local law.
Pay Range: $95,100.00 - $180,700.00 annually Starting rate of pay may vary based on factors including, but not limited to, position offered, location, education, training, and/or experience. For information regarding our benefit programs and eligibility, please visit https://talent.lowes.com/us/en/benefits.
Salary Context
This $95K-$180K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Lowe's Home Improvement, 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 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($137K) sits 35% below the category median. Disclosed range: $95K to $180K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Lowe's Home Improvement AI Hiring
Lowe's Home Improvement has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Charlotte, NC, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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