AI Intern at BlackBox

, , India -

Full Time

Start Date

Immediate

Expiry Date

22 Dec, 25

Salary

0.0

Posted On

23 Sep, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Azure, Data Engineering, Backend Development, Testing and QA, Self-Starter, Communication, Adaptability, Problem-Solving Mindset, RAG, Prompt Engineering, Predictive Modeling, Frontend Development, Cloud Knowledge, Advanced AI Concepts, Documentation & Compliance

Industry

IT Services and IT Consulting

Description

As an AI Engineer Intern, you will support the integration, deployment, and operationalization of foundational AI models and components, with a focus on Generative AI, Large Language Models (LLMs), Small Language Models (SLMs), and prompt engineering for conversational AI and workflow automation. Working alongside experienced team members, you will help ensure smooth implementation and takeover of AI solutions, while also contributing to documentation and extending AI capabilities across various business applications. This role offers a valuable opportunity to gain hands-on experience in building scalable AI solutions within an enterprise environment. 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. Deployment & Operationalization Production Deployment: Manage deployment workflows to ensure AI models and services are successfully moved to production environments, using Azure tools for seamless deployment. Monitoring & Maintenance: Set up monitoring mechanisms to track the performance and health of AI models in production, ensuring timely updates or adjustments based on performance metrics and new data. Lifecycle Management: Document and manage the lifecycle of each AI solution, ensuring proper version control, retraining protocols, and deprecation processes as needed. Innovation & Self-Development Continuous Learning: Stay current on foundational AI models, RAG techniques, and Azure AI capabilities to enhance the solutions you develop. Problem Solving & Initiative: Proactively identify areas for improvement in workflows and processes, suggesting innovative ideas to enhance solution effectiveness and efficiency. Ownership & Autonomy: Take full ownership of assigned use cases, handling all aspects of development, testing, and deployment with minimal supervision. Required Skills 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., Typescript, 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.

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Responsibilities

The AI Intern will support the integration, deployment, and operationalization of foundational AI models, focusing on Generative AI and Large Language Models. Responsibilities include developing data pipelines, integrating models, and ensuring smooth implementation of AI solutions.