Senior AI Engineer

CI Financial•Toronto, ON

40d•CA$69,500 - CA$119,500•Hybrid

About The Position

CI Financial is seeking an experienced and technically strong Senior AI Engineer to join our Data, AI & Analytics (DNA) team. This role focuses on the applied use of Generative AI and Agentic AI, leveraging existing large language models (LLMs) and platforms—not building models from scratch. As a senior member of the team, you'll take technical ownership of intelligent applications and automation tools that use LLMs to solve business problems, set best practices for the team, and mentor junior engineers, while partnering closely with business, engineering, and governance stakeholders to bring production-grade AI solutions to life. This opportunity is for an existing vacancy with the company. CI Financial is an independent company offering global wealth management and asset management advisory services through diverse financial services firms. Since 1965, we have consistently anticipated and responded to the changing needs of investors. We are driven by a commitment to provide individuals and institutions with the highest-quality investments and advice. Our commitment to the highest levels of performance means that whatever their position, CI employees must be comfortable in a fast-paced environment that will stretch them to tap into their highest potential. Employees with a healthy dose of ambition, a desire to commit to a curious mindset for continuous learning, and a willingness to go the extra mile thrive at CI.

Requirements

Bachelor's or Master's degree in Computer Science, Data Science, AI, Machine Learning, Statistics, or a related field.
4–8+ years of experience in a data science, machine learning, or AI engineering role, including hands-on experience delivering production systems.
Demonstrated experience designing, building, and shipping agentic AI systems involving multi-step reasoning, task decomposition, and tool use.
Experience designing around real-world infrastructure limitations—such as rate limits, quota management, retries/backoff, and cost-per-call—when architecting LLM-powered systems at scale.
Deep understanding of prompt engineering, RAG, fine-tuning, and evaluation of generative output, including tradeoffs across approaches.
Proficiency in Python and ML/AI libraries (e.g., Scikit-learn, PyTorch, TensorFlow).
Hands-on skills in designing and orchestrating agentic workflows (multi-step reasoning, task decomposition, tool/function calling).
Excellent communication skills; able to present complex technical concepts clearly to both technical and non-technical stakeholders, including senior leadership).
Experience with cloud-based environments (AWS, Azure, or GCP).

Responsibilities

Architect and build production-grade intelligent systems and applications using existing LLMs (e.g., OpenAI, Claude, Gemini, etc.) through API integration, prompt engineering, fine-tuning, or Retrieval-Augmented Generation (RAG).
Design, prototype, and lead development of agentic AI systems capable of multi-step reasoning, task decomposition, and tool use to automate and support complex business workflows.
Contribute to the design of a reusable, scalable AI platform (shared tooling, agent frameworks, and infrastructure) rather than one-off point solutions, so capabilities can be reused across use cases.
Build web app AI solutions using frameworks such as React or Streamlit.
Set technical direction and best practices for GenAI and agentic solution design, including evaluation methodology, guardrails, and reliability of LLM-powered systems.
Drive continuous iteration and improvement of models and LLM-powered tools based on performance metrics, cost, and user feedback.
Lead design and code reviews, championing best practices around experimentation, reproducibility, and responsible AI.