Applied Machine Learning Scientist II

TD•Toronto, ON

5d•Onsite

About The Position

We are looking for a highly motivated Applied Machine Learning Scientist to join the Wealth AI / ML Practice, focused on Generative AI and Agentic Capabilities. In this role, you will work at the intersection of problem discovery, solution design, and implementation, helping shape how agent‑based and traditional AI solutions are applied across Wealth use cases. You will partner closely with business, technology, and risk stakeholders to evaluate whether a given problem is best solved using Agentic AI, Generative AI, or traditional ML approaches, and then help build the solution accordingly. This is a unique opportunity to work on high‑impact, enterprise‑scale GenAI initiatives, while remaining grounded in strong ML fundamentals and responsible AI practices.

Requirements

2+ years of experience applying machine learning or AI techniques to real‑world business problems
Strong proficiency in Python, with experience building production‑grade data or ML pipelines
Solid foundations in traditional ML (feature engineering, model evaluation, interpretability)
Curiosity and judgment to determine how a problem should be solved—not just how to model it
Experience working with unstructured data (text, documents, PDFs, etc.)
Ability to balance speed and rigor in a regulated or enterprise environment
Undergraduate degree required

Nice To Haves

Advanced technical degree in a STEM field preferred
Experience with Generative AI / LLMs, including RAG or agent‑based patterns
Exposure to agentic frameworks, orchestration tools, or workflow engines
Experience working in financial services or other regulated environments
Familiarity with model governance, validation, or risk controls
Experience integrating AI solutions with downstream systems or APIs

Responsibilities

Lead problem framing and solution design, assessing whether business problems are best addressed through: Agentic workflows (tool‑using or multi‑step reasoning agents), Generative AI solutions (LLMs, RAG, summarization, extraction), Traditional ML approaches (classification, regression, ranking, forecasting)
Design, build, and deploy Agentic solutions, using: Low‑code / platform tools (e.g., Copilot Studio, orchestration frameworks), and/or Pro‑code implementations (Python, APIs, workflows, custom pipelines)
Collaborate with cross‑functional partners (Business, Technology, Risk, Compliance) to: Translate ambiguous problem statements into implementable AI solutions, Define success metrics, guardrails, and validation approaches, Ensure solutions meet governance and responsible AI expectations
Evaluate, test, and monitor solutions in production, ensuring: Performance, robustness, and explainability, Appropriate human‑in‑the‑loop controls where required, Continuous improvement based on usage and outcomes
Communicate complex technical concepts clearly to non‑technical stakeholders, supporting adoption and trust.