Senior Generative AI Engineer - LLMs & Production AI

The Home Depot; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

The Home Depot is seeking a Sr Machine Learning Engineer to design, build, and maintain AI-powered applications that utilize generative models and LLMs to enhance user experience. You will work across product teams to deliver AI-driven features while ensuring reliability and scalability.

Responsibilities include implementing RAG pipelines, model integration, performance optimization, and creating reusable AI workflows.

Qualifications

• 2 years of relevant work experience in ML/AI engineering.
• Experience building Generative AI applications using LLMs.
• Experience with prompt engineering, optimization, and evaluation techniques.
• Experience integrating AI models via APIs (Google/OpenAI/Anthropic).
• Experience with RAG pipelines and vector databases.
• Experience with CI/CD pipelines and automated workflows.

Responsibilities

• Design, build, and maintain AI-powered applications leveraging generative models.
• Collaborate with UX, engineering, and product teams to deliver user stories.
• Develop scalable AI services and monitoring for production systems.
• Embed intelligent capabilities into software products using LLMs and other models.
• Create documentation and enablement materials for generative AI solutions.

Skills

Python

Generative AI

Prompt engineering

API integration

RAG pipelines

Vector databases

CI/CD

NLP

GCP Vertex AI

Frontend basics (React/JS)

SQL

Education

High school diploma / GED

Tools

Google Cloud Platform

Vertex AI

BigQuery

APIs (OpenAI, Google, Anthropic)

Vector databases tools

Job description

The Home Depot is seeking a Sr Machine Learning Engineer to design, build, and maintain AI-powered applications that utilize generative models and LLMs to enhance user experience. You will work across product teams to deliver AI-driven features while ensuring reliability and scalability.

Responsibilities include implementing RAG pipelines, model integration, performance optimization, and creating reusable AI workflows.