Associate AI Engineer

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 (e.g., 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 Node.js 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.