Senior AI Engineer (Remote)

The Home Depot•Atlanta, GA

11d•$100,000 - $250,000•Remote

About The Position

The Senior AI Engineer designs, builds, scales, and optimizes production-grade agentic AI systems to drive business outcomes across The Home Depot. Working across data science, machine learning engineering, and software engineering, this hands-on position turns AI concepts into enterprise-ready products. The role develops scalable applications powered by large and small language models, retrieval-augmented generation frameworks, and autonomous agents. It builds orchestration layers for multi-agent workflows, tool integration, and planning, as well as infrastructure for reliable, large-scale cloud deployment. The engineer partners with product, engineering, and business teams to prototype solutions, work through ambiguity, and bring AI capabilities from concept to production. The position typically reports to a Software Engineer Manager or Senior Software Engineer Manager and has no direct reports. Overnight travel is typically required 5% to 20% of the time. The role is primarily in a comfortable indoor setting, with most time spent sitting and occasional opportunities to move about; on rare occasions, light lifting may be needed.

Requirements

At least 6 years of experience in AI, machine learning engineering, or software engineering, with strong Python development skills and modern software engineering practices.
Proven experience building and deploying production-grade AI solutions using LLMs, SLMs, RAG frameworks, copilots, agents, and multi-agent systems.
Deep understanding of AI/ML foundations, including transformers, embeddings, deep learning, prompt engineering, agentic reasoning patterns, and vector databases.
Experience developing orchestration layers for task execution, routing, planning, and workflows, and integrating AI solutions with enterprise platforms, APIs, and business systems.
Expertise in cloud-native architectures, Docker, Kubernetes/GKE, infrastructure as code, and AI pipeline design.
Hands-on implementation of MLOps/LLMOps practices across the AI and agent lifecycle, including CI/CD, automated testing, model versioning and registries, governance, compliance, and security.
Experience building automated CI/CD pipelines for AI/agentic systems and implementing canary, blue-green, and shadow deployments with automated rollback.
Experience establishing end-to-end observability, including logging, metrics, distributed tracing, and automated alerting across models, agents, and orchestration layers.
Demonstrated ability to optimize complex AI systems for performance, reliability, scalability, latency, cost efficiency, and token use, and to debug operational failure modes.
Excellent cross-functional communication and collaboration skills and ability to take AI solutions from concept to production in complex enterprise environments.
Must be at least 18 years of age.
Must be legally permitted to work in the United States.
Minimum education: knowledge, skills, and abilities typically acquired through completion of a high school diploma and/or GED.
The posting lists a minimum of 2 years of work experience in its minimum-qualifications section, while the required-skills section specifies 6+ years.

Nice To Haves

Hands-on experience with Vertex AI, Gemini, Google ADK, LangGraph, CrewAI, AutoGen, or similar orchestration tools and frameworks.
Hands-on experience with infrastructure as code such as Terraform, Kubernetes/GKE, GPU or accelerator provisioning and autoscaling, model registries, feature stores, and production-scale vector database operations.
Full-stack skills including Node.js, React, REST, API design, performance optimization, Linux, Git, and modern deployment toolchains.
Background in retail, supply chain, manufacturing, eCommerce, logistics, or finance where applied machine learning is mature.
Knowledge and experience establishing Responsible AI practices, evaluation frameworks, reliability engineering, and AI governance guardrails.
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 degree in computer science, artificial intelligence, machine learning, or a related technical discipline.

Responsibilities

Collaborate and pair with UX, engineering, and product management colleagues to create secure, reliable, and scalable machine learning solutions.
Document, review, and ensure compliance with quality and change-control standards.
Work with the product team to make user stories developer-ready, understandable, and testable.
Write custom code or scripts to automate infrastructure, monitoring services, and test cases.
Write code or scripts for destructive testing to assess production resiliency.
Configure commercial off-the-shelf solutions to align with changing business needs.
Create dashboards, logging, alerting, and responses to capture and proactively address issues.
Participate in learning activities related to modern software design, machine learning, and development practices; learn about new technologies and practices used by other technology organizations.
Answer questions from product and support teams, monitor tools, and encourage collaboration across product teams.
Provide application support for production software and proactively monitor product Service Level Objectives.
Review production performance and capacity across code, infrastructure, data, message processing, and prediction quality.
Design, build, scale, and optimize production-grade agentic AI systems and enterprise AI products.
Develop applications using LLMs, SLMs, RAG frameworks, copilots, agents, and multi-agent systems.
Build orchestration layers for task execution, routing, planning, workflows, and tool integration.
Develop cloud infrastructure and deployment practices that support reliable, large-scale AI systems.
Integrate AI solutions with enterprise platforms, APIs, and business systems.
Implement AI/LLMOps practices, including CI/CD, automated testing, model versioning and registries, governance, compliance, and security.
Implement progressive deployment strategies, automated rollback, and end-to-end observability across models, agents, and orchestration layers.
Optimize AI systems for performance, reliability, scalability, latency, cost, and token efficiency, and debug operational failure modes.
Partner with product, engineering, and business teams to take AI solutions from concept through production.