we are looking for candidates who can demonstrate a proactive and hands-on approach to learning ai.
foundational ai/ml in practice: you have moved past theory and have practical, hands-on experience. this must be demonstrated through:
personal or academic projects: you have built projects where you have trained a machine learning model (., using scikit-learn, pytorch) and/or consumed a third-party ai api to create an application.
data handling skills: you have experience using python with libraries like pandas and numpy to clean, manipulate, and prepare data for machine learning tasks.
knowledge of modern ai architectures: you can clearly explain and have a working knowledge of core concepts in the modern ai stack. you must understand:
what large language models (llms) are and the key differences between them.
model context protocol (mcp) fundamentals: you must grasp the core mechanics of how models use context, including:
the concept of a model's context window and its practical limitations.
basic strategies for managing conversation history or state in multi-turn interactions.
the principles of prompt engineering and retrieval-augmented generation (rag).
the core concepts of agentic ai: what it means for an ai to have goals, use tools, and perform multi-step reasoning.
strong programming fundamentals:
proficiency in python: you have used python to build small applications or complex scripts.
familiarity with backend concepts: you understand the basics of building an api and have some exposure to a backend framework. familiarity with is a strong plus.
analytical mindset: you possess a bachelor's degree in computer science, data science, engineering, or a related field, and you know how to break down complex problems logically.