Senior AI Engineer

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

• 2

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