Senior AI Engineer (Remote)
Home Depot · Atlanta · $100,000–$250,000 (stated in the posting)
With a career at The Home Depot, you can be yourself and also be part of something bigger.
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.
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