Black Box Artificial Intelligence Internship Program 2025

Qualifications

Technical Skills

• Programming: Proficiency in Python for data handling and AI-related scripting.
• Azure: Experience with Azure AI services, Cognitive Services, and deployment tools.
• Data Engineering: Familiarity with data pipeline development, ETL processes, and data preprocessing techniques.
• Backend Development: Experience with backend frameworks such as Flask or Django, along with API development knowledge.
• Testing and QA: Knowledge of testing frameworks and tools to ensure model robustness, as well as experience with automated testing for QA processes.

Soft Skills

• Self-Starter: Ability to independently manage tasks and demonstrate initiative in solving problems and completing projects.
• Communication: Strong written and verbal communication skills, especially for documenting processes and coordinating with team members.
• Adaptability: Flexibility to learn and work across different aspects of AI solution development, from data engineering to backend and QA.
• Problem-Solving Mindset: Eagerness to tackle complex challenges and find innovative solutions in a fast-paced environment.

Preferred Skills

• Experience with RAG: Familiarity with Retrieval-Augmented Generation techniques and their applications.
• Prompt Engineering: Knowledge of prompt engineering techniques to optimize generative AI models for specific tasks and enhance output relevance.
• Predictive Modeling: Experience in predictive modeling areas, typically associated with data scientists or ML engineers, such as traditional supervised and unsupervised learning, which can be beneficial in understanding model deployment and integration.
• Frontend Development: Experience with frontend frameworks (e.g., React, Angular, or Vue) and UI/UX best practices for integrating AI into user interfaces.
• Cloud Knowledge: Understanding of cloud services for scalable AI deployment, preferably with Azure.
• Advanced AI Concepts: Basic familiarity with foundational generative AI models and Natural Language Processing (NLP).
• Documentation & Compliance: Experience creating detailed documentation and understanding basic compliance in AI practices.

Job Description & Key Responsibilities:

End-to-End AI Solution Development

• Data Engineering: Independently develop data pipelines, perform data extraction, transformation, and loading (ETL), and preprocess data for AI model consumption.
• Model Integration: Integrate foundational models (e.g., GPT, BERT) and retrieval-augmented generation (RAG) techniques to enable dynamic and context-aware outputs, primarily using Azure AI offerings.
• Backend Development: Implement backend services and API endpoints to support AI solutions, ensuring scalability and security for deployed models.
• Frontend Integration: Develop or adapt frontend interfaces for user interaction with AI models, working with UI/UX designs to create intuitive and user-friendly experiences.

Handover & Knowledge Transfer

• Documentation for Handover: Contribute to detailed documentation of AI solutions to facilitate smooth handovers from other teams or vendors, ensuring clear and organized records of workflows and processes.
• Collaborative Knowledge Sharing: Participate in knowledge-sharing sessions with cross-functional teams, helping to bridge the gap between model development and deployment.

Quality Assurance and Testing

• Testing Protocols: Design and execute testing protocols to ensure model accuracy, reliability, and robustness within deployed solutions.
• Automated Testing: Set up automated testing workflows and QA processes to verify data processing pipelines, model performance, and user-facing functionalities.
• Error Handling & Debugging: Proactively address errors and bugs throughout the development cycle, implementing necessary fixes and documenting issues to maintain quality standards.