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Atlanta, GA

Internship

Onsite

$36K/yr - $65K/yr

Intern

Acuity Inc. is a market-leading industrial technology company that focuses on innovative products and services in lighting and building management solutions. As a Data & AI Engineering Intern, you will assist in architecting data pipelines and preparing data for AI initiatives, contributing to the development of intelligent systems that drive business impact across various domains.

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Responsibilities

Design and Build Data Pipelines: Assist in architecting, implementing, and deploying data pipelines and data models that support Acuity’s analytics and AI initiatives using tools like Azure Data Factory, Databricks, and SQL.
Analyze and Prepare Data: Perform data exploration, transformation, and validation on structured and unstructured data to ensure accuracy and usability for model training and business reporting.
Support Data Quality & Governance: Help monitor and maintain data quality standards, documentation, and lineage; learn and apply best practices for security, compliance, and governance.

Qualification

Required

Currently has, or is in the process of obtaining, a Bachelors, or Masters degree in Computer Science, Machine Learning, Data Science, Computational Engineering, Information Science, or a related technical field
Knowledge of SQL, data modeling, and at least one programming language (e.g., Python, C++, C#, Scala)
Strong foundation in machine learning fundamentals (supervised/unsupervised learning, neural networks, model training, and evaluation)
Demonstrated hands-on experience building or experimenting with AI-powered applications or prototypes (academic, personal, or professional)
Strong problem-solving, collaboration, and communication skills—able to approach open-ended problems with structure and explain complex ideas to diverse teams
Curious, self-driven, analytical and excited to play with data in a fast-paced environment
Experience fine-tuning or prompting large language models (LLMs) such as OpenAI GPT, Claude, or Gemini

Preferred

Exposure to cloud-based ML platforms and tools (e.g., Azure ML, Databricks, Azure Stream Analytics)
Familiarity with vector databases, retrieval-augmented generation (RAG), or AI agent frameworks
Experience with real-time data processing (Event Hubs, Kafka, Delta Live Tables)
Interest in applying AI to business domains like sales, customer experience, product management, or building intelligence