Machine Learning Engineer II – Generative AI (Remote)

The Home Depot

4d•$90,000 - $170,000•Remote

About The Position

With a career at The Home Depot, you can be yourself and also be part of something bigger. Position Purpose: The Machine Learning Engineer II is responsible for designing, building, integrating, optimizing, and maintaining AI-powered applications that leverage generative models and overall product lifecycle for a product that our users love. Generative AI Engineers are expected to collaborate closely with teammates as they develop and deliver user stories while supporting AI-powered products as they evolve. Generative AI Engineers may design and implement applications using large language models (LLMs) and other generative models to embed intelligent capabilities directly into software products. Activities may include prompt engineering, model integration, building Retrieval-Augmented Generation (RAG) pipelines, and developing scalable AI services. The role may interact with business stakeholders, infrastructure teams, and development teams to ensure business requirements are effectively addressed through generative AI solutions. The role may also support evaluation, performance optimization, testing, and monitoring of AI systems in production. Additional responsibilities may include working with domain data, improving prompts and AI workflows, and creating documentation or enablement materials for generative AI solutions. Generative AI Engineers should be able to work independently with minimal guidance, while collaborating with cross-functional teams of varying skill levels to design, deploy, and maintain production AI applications. This role may review submitted code and prompt implementations, providing feedback and improvements based on engineering and responsible AI best practices.

Requirements

Must be eighteen years of age or older.
Must be legally permitted to work in the United States.

Nice To Haves

1–3 years of relevant work experience in Generative AI, Machine Learning, or AI application development
Experience in Python and modern AI development frameworks
Experience building Generative AI applications using large language models (LLMs)
Experience with prompt engineering, prompt optimization, and prompt evaluation techniques
Experience integrating AI models through APIs from platforms such as Google, OpenAI or Anthropic
Experience with GenAI frameworks such as Google Agent Development Kit (ADK)
Experience implementing Retrieval-Augmented Generation (RAG) pipelines using vector databases
Experience working with vector databases such as google Vertex AI Search
Familiarity with building conversational AI systems, or AI assistants
Familiarity with responsible AI practices including bias mitigation and safety guardrails
Familiarity with REST APIs, microservices architecture, and scalable AI system deployment
Familiarity implementing CI/CD pipelines, monitoring, and automated workflows for reliable AI model deployment and lifecycle management.
Familiarity with monitoring, evaluation, and optimization of production AI systems

Responsibilities

Delivery and Execution - 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
Program configuration/modification and setup activities on large projects using HD approved methodology
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
Learning - 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
Support and Enablement - 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