Senior Machine Learning - AI Engineer (Remote)

The Home Depot•COLORADO - VIRTUAL - CO01, CO

1d•$100,000 - $180,000•Remote

About The Position

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.

Requirements

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.

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 (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.
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.

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.