Intern, AI Engineer

The Home Depot

9d•CA$31,600 - CA$35,400•Onsite

About The Position

The Home Depot Canada Internship/Co-op Program is a 15-week opportunity to apply classroom learning in a dynamic, hands-on environment. Interns will work on impactful projects, sharpen their skills, and build confidence for their future careers. The program includes comprehensive onboarding, strategic projects addressing business challenges, project presentations to senior leadership, professional development sessions, and networking opportunities with mentors and leaders. HD Tech is responsible for the technology behind HomeDepot.ca and its supporting applications, focusing on generative AI in production on Google Cloud. This role involves building agents in Java with Google’s Agent Development Kit (ADK), developing Spring Boot services, and managing data pipelines. It is an engineering role with real users, scale, and constraints, suitable for individuals who write Java, are curious about shipping LLM agents, and want their code used by thousands of people.

Requirements

Currently enrolled in a Canadian university or college co-op program in Computer Science, Software Engineering, or a closely related technical field, with AI coursework, specialization, or project work
Strong programming fundamentals in Java (17 or later): data structures, algorithms, object-oriented design, and the ability to write clean, testable code
Working knowledge of how LLMs are used in real applications: prompting, context management, tool/function calling, and the fundamentals of retrieval-augmented generation
Experience building server-side applications and REST APIs, ideally with Spring Boot
Familiarity with a JVM build tool (Maven or Gradle) and unit testing with JUnit
SQL for retrieving and shaping data
Comfortable with Git and GitHub (branching, pull requests, code review) and a modern IDE such as IntelliJ IDEA or VS Code
A portfolio of things you have built: GitHub repositories, side projects, hackathons, research, or open-source contributions
Sound engineering judgment—you can debug systematically, read unfamiliar code, and reason about trade-offs
Strong problem-solving skills and genuine curiosity about where this technology is heading
Excellent communication skills, with the ability to explain technical decisions to non-technical audiences
Must be legally entitled to work in Canada by way of Canadian citizenship, permanent residency, or a valid study/work permit.

Nice To Haves

Hands-on experience with Google’s Agent Development Kit (ADK) for Java, or another agent framework such as LangChain4j, Spring AI, or LangGraph
Google Cloud experience, particularly Gemini Enterprise Agent Platform (formerly Vertex AI), BigQuery, and Google Kubernetes Engine
Angular, or another modern TypeScript front-end framework, for building internal tools and agent interfaces
Data pipeline experience with Apache Beam, Cloud Dataflow, Spark, or similar
Familiarity with the Model Context Protocol (MCP) or agent-to-agent communication patterns
Familiarity with embeddings and vector search (Agent Platform Vector Search, pgvector, or FAISS)
Containerization with Docker and Kubernetes, and familiarity with CI/CD pipelines
Some Python for prototyping or scripting
Experience evaluating or monitoring LLM applications, whether with an existing framework or a harness you wrote yourself
Awareness of responsible AI topics: bias, privacy, evaluation, and adversarial inputs such as prompt injection

Responsibilities

Collect, clean, and preprocess large datasets from various sources
Build AI agents in Java using Google’s Agent Development Kit (ADK): prompt design, tool/function calling, and multi-step task orchestration
Perform exploratory data analysis (EDA) to uncover trends, patterns, and insights
Develop Spring Boot microservices that expose agents and models to downstream applications through REST APIs, and deploy them as containers on Google Kubernetes Engine (GKE)
Contribute to building, training, and evaluating machine learning models (e.g., tree-based models, deep learning, time series forecasting, optimization/operations research techniques) or developing basic AI agent workflows
Implement retrieval-augmented generation (RAG): document chunking, embeddings, and vector search over Home Depot Canada product and content data
Collaborate with cross-functional teams (between business and technology) to translate data insights into actionable recommendations
Build batch and streaming data pipelines in Java (Apache Beam on Cloud Dataflow) that feed BigQuery and agent workloads
Develop data visualizations and dashboards to communicate findings to technical and non-technical stakeholders
Integrate agents with internal systems, including exposing and consuming tools over the Model Context Protocol (MCP)
Document methodologies and present findings to the team
Evaluate agent behaviour: build test cases and measure accuracy, groundedness, latency, and cost, then iterate on the results
Apply responsible AI and security practices, including PII handling, hallucination mitigation, prompt-injection awareness, and safe failure modes
Collaborate with engineers, product managers, and business partners; document design decisions and present results to the team

Benefits

Comprehensive Onboarding
Strategic Projects
Project Presentation
Professional Development Sessions
Networking Opportunities
Coaching and mentoring
Performance feedback
Leadership and development opportunities
Potential for a full-time offer upon graduation
Practical experience taking an AI feature from prototype to production, including evaluation, deployment, and monitoring.