AI Intern USA

Black Box•Plano, TX

3d•Onsite

About The Position

Black Box Network Services is a leading global communications system integrator specializing in designing, sourcing, implementing, and managing complex technology solutions. As part of our strategic transformation, Black Box is expanding its AI Center of Excellence (CoE) to deliver enterprise-grade AI solutions across multiple business domains. The AI CoE focuses on building scalable, secure, and production-ready AI systems, establishing best practices for enterprise AI adoption, and integrating AI capabilities into core business platforms. As an AI Engineer Intern in the AI Center of Excellence (CoE), you will contribute to the design, development, and integration of applied AI solutions using pre-trained Large Language Models (LLMs), traditional machine learning techniques, and deterministic approaches. This role offers hands-on experience building enterprise-grade Generative AI solutions across backend services, data pipelines, orchestration, and user-facing applications. Working closely with experienced AI engineers, you will contribute to real-world AI use cases integrated with platforms such as ServiceNow, SAP, Salesforce, and Azure services. This internship is designed to strengthen applied AI engineering skills and prepare candidates for conversion into a full-time AI Engineer role.

Requirements

Currently pursuing a Master’s degree in Engineering or a related field (Computer Science, Artificial Intelligence, Data Science, or similar).
Must have at least 6 months remaining to complete the Master’s program at the time of joining.
Must have a minimum of 2 years of relevant professional experience between Bachelor’s and Master’s programs.
Prior experience must include applied AI / Machine Learning, with hands-on exposure to Generative AI use cases.
Available for a full-time, on-site internship for a minimum of 6–12 months (depending on academic program constraints).
Programming: Strong working knowledge of Python.
Applied AI / GenAI: Hands-on experience building or integrating ML or Generative AI solutions.
Generative AI: Practical experience with LLMs, prompt engineering, and/or RAG-based architectures.
Backend Development: Experience building APIs using FastAPI, Flask, or Node.js (TypeScript).
Frontend Development: Working experience building React-based user interfaces and integrating them with backend APIs.
Data Handling: Experience working with structured and unstructured data, including basic preprocessing or ETL.
APIs & Cloud: Experience consuming REST APIs and familiarity with cloud platforms (Azure preferred).
2+ years of relevant professional experience between Bachelor’s and Master’s programs.
Experience in applied AI, machine learning, or software engineering with AI components.
Ability to translate AI concepts into working prototypes or production-ready solutions.
Strong learning mindset, ownership, and clear communication with a structured problem-solving approach.

Nice To Haves

Familiarity with NLP concepts and foundational Generative AI models.
Awareness of responsible AI and basic AI governance concepts.
Exposure to Microsoft Power Platform or low-code automation tools.

Responsibilities

Build and integrate AI solutions using pre-trained LLMs for conversational AI, summarization, and enterprise knowledge retrieval.
Implement RAG-based architectures connecting LLMs with structured and unstructured enterprise data.
Develop and test AI agents, traditional ML models, and deterministic logic for real-world use cases.
Contribute to AI orchestration using LangChain and workflow automation using n8n.
Build AI-enabled user interfaces and integrate them with backend services.
Develop and maintain backend APIs and services.
Integrate AI solutions with enterprise platforms such as ServiceNow, SAP, Salesforce, and Azure services.
Build and maintain data pipelines, including preprocessing and quality checks.
Support testing, debugging, deployment, and monitoring of AI services on Azure.
Document AI workflows, integrations, and solution lifecycle updates.
Collaborate with AI, data, and platform teams to deliver production-ready AI solutions.
Continuously learn and apply best practices in Generative AI, RAG patterns, and enterprise AI systems.