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About This Role
Position Purpose:
The Senior AI Engineer is responsible for designing, building, scaling, and optimizing production\-grade Agentic AI systems that drive measurable business outcomes across The Home Depot. Operating at the intersection of Data Science, Machine Learning Engineering, and Software Engineering, this hands\-on role translates AI concepts into enterprise\-ready products.
This role involves developing scalable applications powered by LLMs, SLMs, Retrieval\-Augmented Generation (RAG) frameworks, and autonomous agents. You will build the core orchestration layers for multi\-agent workflows, tool integration, and planning, alongside the infrastructure required for reliable, large\-scale cloud deployment. By partnering with product, engineering, and business teams, you will rapidly prototype solutions, navigate ambiguity, and seamlessly transition cutting\-edge AI capabilities from concept to production.
Required skills
Experience: 6\+ years of experience in AI, Machine Learning Engineering, or Software Engineering with strong Python development skills and modern software engineering practices.
AI Delivery: Proven experience building and deploying production\-grade AI solutions using LLMs, SLMs, RAG frameworks, copilots, agents, and multi\-agent systems.
AI Foundations: Deep understanding of AI/ML foundations, including transformers, embeddings, deep learning, prompt engineering, agentic reasoning patterns, and vector databases.
Orchestration \& Integration: Experience developing orchestration layers (task execution, routing, planning, workflows) and seamlessly integrating AI solutions with enterprise platforms, APIs, and business systems.
Infrastructure \& MLOps: Expertise in cloud\-native architectures, containerization (Docker) and orchestration (Kubernetes/GKE), infrastructure as code (e.g., Terraform), and AI pipeline design, with hands\-on implementation of MLOps/LLMOps best practices (CI/CD, automated testing, model versioning and registries, governance, compliance, and security) across the full AI/agent lifecycle.
AIOps \& Deployment Reliability: Experience building automated CI/CD pipelines for AI/agentic systems, implementing progressive rollout strategies (canary, blue\-green, and shadow deployments) with automated rollback, and establishing end\-to\-end observability (logging, metrics, distributed tracing, and automated alerting) across models, agents, and orchestration layers to ensure production reliability, performance, and cost/token efficiency at scale.
Optimization \& Debugging: Demonstrated ability to optimize complex AI systems for performance, reliability, scalability, latency, cost efficiency, and token use, as well as debugging operational failure modes.
Execution \& Collaboration: Excellent cross\-functional communication and collaboration skills, with a proven ability to take AI solutions from concept to production in complex enterprise environments.
Key Responsibilities:
70% Delivery and Execution \- Collaborates and pairs with other product team members (UX, engineering, and product management) to create secure, reliable, scalable machine learning solutions; Documents, reviews, and ensures that all quality and change control standards are met; Works with Product Team to ensure user stories that are developer\-ready, easy to understand, and testable; Writes custom code or scripts to automate infrastructure, monitoring services, and test cases; Writes custom code or scripts to do "destructive testing" to ensure adequate resiliency in production; Configures commercial off the shelf solutions to align with evolving business needs; Creates meaningful dashboards, logging, alerting, and responses to ensure that issues are captured and addressed proactively
10% Learning \- Participates in learning activities around modern software design, machine learning, and development core practices (communities of practice); Proactively views articles, tutorials, and videos to learn about new technologies and best practices being used within other technology organizations
20% Support and Enablement \- Fields questions from other product teams or support teams; Monitors tools and participates in conversations to encourage collaboration across product teams; Provides application support for software running in production; Proactively monitors production Service Level Objectives for products; Proactively reviews the Performance and Capacity of all aspects of production: code, infrastructure, data, message processing, and prediction quality
Direct Manager/Direct Reports:
This Position typically reports to Software Engineer Manager or Sr. Software Engineer Manager
This Position has 0 Direct Reports
Travel Requirements:
Typically requires overnight travel 5% to 20% of the time.
Physical Requirements:
Most of the time is spent sitting in a comfortable position and there is frequent opportunity to move about. On rare occasions there may be a need to move or lift light articles.
Working Conditions:
Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable.
Minimum Qualifications:
Must be eighteen years of age or older.
Must be legally permitted to work in the United States.
Preferred Qualifications:
Tools \& Frameworks: Hands\-on experience with Vertex AI, Gemini, Google ADK, LangGraph, CrewAI, AutoGen, or similar orchestration tools and frameworks.
AI Infrastructure \& Platform Tooling: Hands\-on experience with infrastructure\-as\-code (e.g., Terraform), Kubernetes/GKE for container orchestration, GPU/accelerator provisioning and autoscaling, model registries, feature stores, and vector database operations at production scale.
Full‑stack skills: Node.js/React/REST, API design, performance optimization, Linux, Git, modern deployment toolchain.
Industry Context: Background in retail, supply chain, manufacturing, eCommerce, logistics, or finance where Applied ML is mature.
Guardrails \& Reliability: Knowledge and experience in establishing Responsible AI, evaluation frameworks, reliability engineering, and AI governance guardrails.
Leadership \& Innovation: A proven track record of driving innovation, delivering measurable business impact, mentoring engineering teams, and establishing AI engineering standards and best practices.
Master’s or bachelor’s in computer science, Artificial Intelligence, Machine Learning, or a related technical discipline.
Minimum Education:
The knowledge, skills and abilities typically acquired through the completion of a high school diploma and/or GED.
Preferred Education:
No additional education
Minimum Years of Work Experience:
2
Preferred Years of Work Experience:
No additional years of experience
Minimum Leadership Experience:
None
Preferred Leadership Experience:
None
Certifications:
None
Competencies:
Global Perspective
Manages Ambiguity
Nimble Learning
Self\-Development
Collaborates
Cultivates Innovation
Situational Adaptability
Communicates Effectively
Drives Results
Interpersonal Savvy
Benefits offered include health care benefits, 401K, ESPP, paid time off, and success sharing bonus. For a full list of the various benefits The Home Depot offers, visit https://careers.homedepot.com/our\-benefits.
Apply End Date: 09/11/2026
Salary Context
This $100K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At The Home Depot, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($140K) sits 36% below the category median. Disclosed range: $100K to $180K.
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.
The Home Depot AI Hiring
The Home Depot has 4 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Atlanta, GA, US, Denver, CO, US. Compensation range: $170K - $190K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 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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