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About This Role
Company Background:
Established in 1928, Genuine Parts Company is a leading global service provider of automotive and industrial replacement parts and value\-added solutions. Our Automotive Parts Group operates across the U.S., Canada, Mexico, Australasia, France, the U.K., Ireland, Germany, Poland, the Netherlands, Belgium, Spain and Portugal, while our Industrial Parts Group serves customers in the U.S., Canada, Mexico and Australasia. We keep the world moving with a vast network of over 10,700 locations spanning 17 countries supported by more than 63,000 teammates. Learn more at genpt.com.
Position Purpose:
Seeking world\-class talent to join the world’s leading distributor of automotive and industrial replacement parts and value\-added services operating 5,500\+ locations and servicing more than 20,000 locations in the U.S and Canada. Specifically, this role will Lead the strategy, engineering, and applied AI capabilities that power search, discovery, and product finding platforms across digital channels. Drive measurable business outcomes through relevance optimization, search platform modernization, machine learning, GenAI, and agentic commerce capabilities.
This is an engineering leadership role with responsibility for implementing the technology strategy and execution for GPC’s unified commerce platforms.
This individual must be a technologist \& engineer at heart and be comfortable in understanding the technology direction and being hands on with the execution of the strategy. They must exhibit a deep understanding of modern technology stack and agile delivery models, demonstrated focus on customer experience, and must have a proven track record of modernizing legacy supply chain technologies at scale.
Close collaboration and alignment with a wide variety of both internal stakeholders and external vendors will be required. As such, exceptional abilities in building and maintaining strong working relationships and organizational savvy will be required. High level communication and presentation skills are required. Ability to attract, retain, and develop engineering talent will be critical.
Responsibilities:
- Partner with Product and cross\-functional GPC teams to define and execute a vision for AI\-powered search that improves conversion, revenue, and customer productivity.
- Implement the technology platforms architecture and execute on roadmap to support build out the new commerce related capabilities needed for the unified commerce strategy.
- Lead the engineering teams responsible for search platform architecture, scalability, reliability, and performance.
- Drive modernization of search infrastructure leveraging cloud\-native and AI\-enabled technologies.
- Build and scale Search Science capabilities including relevance tuning, ranking models, semantic search, vector search, recommendations, and personalization.
- Responsible for delivery and support of the platform
- Lead multiple pods and manage engineering managers.
- Partner with peer leaders to accelerate and embed agile methodologies across the organization to increase customer\-backed problem solving, to increase pace of solution delivery, to accelerate innovation and to enhance cross\-functional collaboration.
- Recruit, manage, and motivate a team of engineers and Engineering managers to develop best\-in\-class solutions.
- Other duties as assigned.
Location:
- GPC has two work locations to choose from, Duluth or Atlanta office.
- We offer a Flexible Work Policy that permits eligible employees to work remotely.
Desired Qualifications \& Experiences:
- Degree in computer science/engineering or equivalent experience
- 10\+ years’ experience in software engineering delivery (including 3\+ years in leadership role)
- Consistent track record of leadership, teamwork, and delivering high impact results.
- Extensive experience building enterprise grade systems/applications supporting business priorities.
- Experience managing 25\+ FTE team of internal and extended delivery resources.
- Experience in leading leaders. Ability to effectively share technical information, communicate technical issues and solutions to all levels of business.
- Ability to attract, motivate and retain world\-class IT talent.
- Able to juggle multiple projects \- can identify primary and secondary objectives, prioritize time, and communicate timeline to team members.
- Experience in understanding of Information Retrieval, Search Relevance, Ranking Systems, Recommendations, and Personalization.
- Must also have broad and deep technical understanding of the technologies in this field, including but not limited to
+ Familiarity with search technologies such as Elasticsearch, Apache Solr, Google Cloud Retail Search, vector search platforms, and ML ecosystems.
+ Strong knowledge of APIs, microservices, distributed systems, and data engineering.
+ Experience in GenAI, LLMs, Graph RAG, agentic workflows, and conversational commerce.
+ Experience with semantic search, vector databases, embeddings, Retrieval\-Augmented Generation (RAG), and LLM\-powered experiences.
+ Experience in designing event driven mechanisms, publish/subscribe models.
+ Experience with cloud computing on at\-least one of major providers (Google Cloud Platform, AWS or Azure).
+ Experience working with modern SQL / noSQL Databases.
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GPC conducts its business without regard to sex, race, creed, color, religion, marital status, national origin, citizenship status, age, pregnancy, sexual orientation, gender identity or expression, genetic information, disability, military status, status as a veteran, or any other protected characteristic. GPC's policy is to recruit, hire, train, promote, assign, transfer and terminate employees based on their own ability, achievement, experience and conduct and other legitimate business reasons.
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 Genuine Parts Company, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Genuine Parts Company AI Hiring
Genuine Parts Company 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, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 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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