RBC’s 2025 Investor Day reaffirmed our strategic ambition to become a client‑centric, future‑ready leader by harnessing data, digital innovation, and AI to deliver unparalleled value. Our commitment to technological excellence—recognized globally as a top‑three bank for AI maturity—positions us at the forefront of an AI‑informed banking era. With a bold goal to generate up to $1billion in enterprise value from AI by 2027, we are accelerating investments in transformative technologies to redefine industry standards. To fuel this vision we are seeking a highly motivated Winter Intern to join our AiXel team and contribute to cutting‑edge projects in advanced analytics for risk management. This role offers a unique opportunity to apply data science and software engineering skills in real‑world applications, directly impacting risk management workflows through solutions like generative AI, machine learning, and deep learning. What will you do?Participate in the end‑to‑end development of high‑impact AI solutions, from idea design and PoC to production deployment. Work closely with business users to understand their needs, translate business use cases into practical technical problems, and iterate on solutions based on feedback. Focus on building real‑world applications that address business challenges, rather than conducting pure research. Continuously learn and keep up with the latest advancements in AI and related technologies, sharing knowledge with the team. Present technical solutions and project updates to both technical peers and senior management. What do you need to succeed?Must‑have hands‑on experience prototyping GenAI applications, including working with foundational LLMs (e.g., GPT models via API) and transformer models (e.g., Hugging Face Transformers); familiarity with frameworks and tools (e.g., LangChain, LangGraph, LlamaIndex, Haystack) and vector databases (e.g., Weaviate, PGVector). Hands‑on experience with prompt engineering, including designing and refining prompts to optimize LLM outputs. Hands‑on experience developing modular, robust, and scalable software in Python3.x. Knowledge of modular RAG (Retrieval‑Augmented Generation) and agentic systems. Knowledge of professional software engineering best practices across the software development lifecycle, including coding standards, testing methods, code reviews, and version control. Knowledge of machine learning and deep learning algorithms (e.g., supervised methods such as decision trees, gradient boosting, deep neural networks; unsupervised methods such as clustering and dimensionality reduction), as well as natural language processing techniques (e.g., TF‑IDF, transformer models, embedding models). Demonstrated willingness and ability to quickly learn and adapt to new advancements in ML/DL/GenAI. Strong logical thinking skills and attention to detail. Effective communication skills and a collaborative, team‑oriented attitude. A bachelor’s or master’s degree or higher in computer science, engineering, statistics, or a related field is preferred. Nice‑to‑have knowledge of embedding model fine‑tuning, Model Context Protocol (MCP), LLM performance evaluation. Experience deploying GenAI applications in production environments and supporting enterprise‑scale use cases. Hands‑on experience implementing solutions using modern ML/DL frameworks and tools, such as PyTorch, JAX, TensorFlow, scikit‑learn, or Hugging Face Transformers. Experience working in regulated or governed environments. Familiarity with financial risk management concepts. Additional DetailsThis is a 4‑month Fall2026 Student placement (September2026–December2026). The successful candidate must be located within Ontario for the duration of the work term. Application deadline: June14,2026.#J-18808-Ljbffr