Note: The job is a remote job and is open to candidates in USA. The Home Depot is seeking a Senior AI Engineer responsible for designing, building, scaling, and optimizing production-grade Agentic AI systems. This role involves developing scalable applications powered by LLMs and collaborating with product and engineering teams to transition AI capabilities from concept to production.
Responsibilities
- 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
- 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
- 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
Skills
- 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 (task execution, routing, planning, workflows) and seamlessly integrating AI solutions with enterprise platforms, APIs, and business systems
- 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
- 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
- Demonstrated ability to optimize complex AI systems for performance, reliability, scalability, latency, cost efficiency, and token use, as well as debugging operational failure modes
- Excellent cross-functional communication and collaboration skills, with a proven ability to take AI solutions from concept to production in complex enterprise environments
- Must be eighteen years of age or older
- Must be legally permitted to work in the United States
- 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 (e.g., Terraform), Kubernetes/GKE for container orchestration, GPU/accelerator provisioning and autoscaling, model registries, feature stores, and vector database operations at production scale
- Node.js/React/REST, API design, performance optimization, Linux, Git, modern deployment toolchain
- Background in retail, supply chain, manufacturing, eCommerce, logistics, or finance where Applied ML is mature
- Knowledge and experience in establishing Responsible AI, evaluation frameworks, reliability engineering, and AI governance guardrails
- 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
Benefits
- Health care benefits
- 401K
- ESPP
- Paid time off
- Success sharing bonus
Company Overview