Minimum qualifications:

• Bachelor's degree or recent completion in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, Mathematics, or a related field, or equivalent practical experience.
• Strong programming fundamentals and at least one meaningful AI, ML, data, or software project.
• Basic exposure to AI, ML, LLMs, RAG, data engineering, backend engineering, or product engineering through projects, internships, coursework, or self-learning.
• Comfortable writing code in Python, JavaScript, TypeScript, or a similar language.
• Basic understanding of APIs, JSON, databases, Git, and software development workflows.
• Curiosity for modern AI systems and the ability to learn from a team building reliable product features.

Job Description:

As an AI Intern at Aiotrix, you will begin your career by working with intelligent systems that combine machine learning, large language models, retrieval, automation, and practical software engineering. This role is designed for fresh graduates who are curious, disciplined, and excited to grow inside a hands-on AI engineering environment.

You will support engineering, product, and AI teams on experiments, assistants, workflow prototypes, evaluation cases, and product integrations. You will learn how AI ideas are tested, refined, documented, and gradually moved toward reliable product capabilities.

Responsibilities:

• Assist with AI-powered prototypes using LLM APIs, embeddings, retrieval systems, and automation workflows.
• Help develop prompts, structured outputs, small RAG experiments, evaluation cases, and model integration logic under guidance.
• Prepare, clean, transform, and validate data for AI and machine learning workflows.
• Work with backend, frontend, and AI engineers to understand how AI capabilities are integrated into real applications.
• Test AI behavior, identify failure cases, record observations, and document system behavior clearly.
• Support experiments around assistants, predictive systems, document intelligence, and intelligent automation.
• Follow Git-based development practices and participate in code reviews, documentation, and team rituals.
• Stay updated with emerging AI tools, model capabilities, engineering patterns, and responsible AI practices.

Preferred qualifications:

• Understanding of machine learning fundamentals, LLMs, prompt engineering, embeddings, or retrieval-augmented generation.
• Hands-on exposure to tools such as LLM APIs, Pydantic, LangChain, LlamaIndex, Scikit-learn, PyTorch, TensorFlow, or similar tools is a plus.
• Ability to learn quickly, debug patiently, and turn experiments into usable features.
• Strong communication skills and the ability to explain AI behavior, limitations, and trade-offs clearly.
• Portfolio projects, internships, research work, or hackathon builds in AI, ML, data, or software engineering will be valued.