Senior AI Engineer (Remote)

About this role

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, manufactu

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