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Senior AI Engineer

Home Depot / THD Denver, CO 🇺🇸 Remote

Denver, CO

Today

Full time AI Ai Ml API

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Job Description

Senior AI Engineer role in Denver, CO (remote) at Home Depot / THD, focused on designing, building, and optimizing production-grade agentic AI systems to drive measurable business outcomes.

Responsibilities

70% Delivery and Execution: Collaborate with UX, engineering, and product management to create secure, reliable, and scalable ML solutions; document and review to meet quality and change control standards; ensure user stories are developer-ready, clear, and testable; write custom code or scripts to automate infrastructure, monitoring services, and test cases.
10% Learning: Engage in learning activities around modern software design, machine learning, and development best practices; proactively review articles, tutorials, and videos to stay current with technologies used in other organizations.
20% Support and Enablement: Field questions from product and support teams; monitor tools and foster cross-team collaboration; provide production application support; monitor production SLOs; review performance and capacity across code, infrastructure, data, messaging, and prediction quality.

Requirements

Experience: 6+ years in AI, Machine Learning Engineering, or Software Engineering with strong Python development skills and modern software practices.
AI Delivery: Proven track record building and deploying production-grade AI solutions using LLMs, SLMs, RAG frameworks, copilots, agents, and multi-agent systems.
AI Foundations: Deep understanding of transformers, embeddings, deep learning, prompt engineering, agentic reasoning patterns, and vector databases.
Orchestration & Integration: Experience developing orchestration layers (task execution, routing, planning, workflows) and integrating AI solutions with enterprise platforms, APIs, and business systems.
Infrastructure & MLOps: Expertise in cloud-native architectures, Docker, Kubernetes/GKE, Terraform, and AI pipeline design; hands-on MLOps/LLMOps practices across CI/CD, automated testing, model versioning and registries, governance, security, and lifecycle management.
AIOps & Deployment Reliability: Background building automated CI/CD for AI/agent systems; implement canary, blue-green, and shadow deployments with automated rollback; establish end-to-end observability (logging, metrics, tracing, alerting) across models, agents, and orchestration for reliability and cost efficiency at scale.
Optimization & Debugging: Ability to optimize complex AI systems for performance, reliability, scalability, latency, cost, and token efficiency; diagnose and resolve operational failure modes.
Execution & Collaboration: Excellent cross-functional communication and collaboration skills to bring AI solutions from concept to production in large enterprise environments.

Technologies

Python, LLMs, SLMs, RAG frameworks
Vertex AI, Gemini, Google ADK, LangGraph, CrewAI, AutoGen
Node.js, React, REST
Linux, Git, Docker, Kubernetes, GKE
Terraform, Transformers, Embeddings, Vector databases

Benefits

Health care benefits
401K
ESPP
Paid time off
Success sharing bonus

Travel Requirements

Typically requires overnight travel 5% to 20% of the time

Physical Requirements

Most of the time spent sitting in a comfortable position; frequent movement possible; occasional lifting of light objects

Working Conditions

Located in a comfortable indoor area; infrequent and unobjectionable exposure to any unpleasant conditions

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 with Vertex AI, Gemini, Google ADK, LangGraph, CrewAI, AutoGen, or similar orchestration tools
AI Infrastructure & Platform Tooling: IaC (Terraform), Kubernetes/GKE, GPU provisioning and autoscaling, model registries, feature stores, vector databases at production scale
Full-stack Skills: Node.js/React/REST, API design, performance optimization, Linux, Git, modern deployment toolchains
Industry Context: Retail, supply chain, manufacturing, eCommerce, logistics, or finance with mature Applied ML
Guardrails & Reliability: Responsible AI, evaluation frameworks, reliability engineering, AI governance guardrails
Leadership & Innovation: Track record of driving innovation, delivering business impact, mentoring teams, establishing AI engineering standards
Education: Master’s or bachelor’s in computer science, AI, ML, or related field

Minimum Education

High school diploma or GED

Preferred Education

No additional education

Minimum Years of Work Experience

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

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