- We are looking for an Applied AI Engineer to join the AI Lab team within DerivativeEDGE

- Reporting to the AI Lab lead, you will work closely with Engineering, Product, and domain experts to design, build, and ship AI-powered features in a complex, high-stakes financial setting

- This is a hands-on engineering role

- You will move between experimentation and production, work through ambiguous problems, and deliver AI capabilities that real clients use

- Generic approaches do not get you far here
- The expectation is that you learn the domain, move with urgency, and build things that hold up
- The core of this role sits at the intersection of Generative AI, Agentic Workflows, and Data Engineering: building LLM-powered solutions, designing agents that can reason and act across complex workflows, and making sure the right data is available to support it all

- Applied to financial derivatives, these are genuinely hard problems worth solving

- Build and ship LLM-powered features across the DerivativeEDGE platform, working directly with domain experts to translate complex financial workflows into reliable AI capabilities

- Design and implement agentic workflows that can reason, act, and recover gracefully across multi-step processes in a production environment

- Own the data engineering layer that feeds AI systems: pipelines, retrieval architectures, context design, and data quality

- Move fluidly between experimentation and production. You will prototype quickly, evaluate honestly, and know when something is ready to ship

- Contribute to the AI Lab’s broader technical direction, including evaluations, tooling, MLOps practices, and the patterns the team builds on





- Depending on where projects take you, work may also touch NLP, model fine-tuning, synthetic data, or reinforcement learning. Curiosity and a willingness to build in new areas are expected- Working knowledge of Python and common AI/ML frameworks (PyTorch, HuggingFace, LangChain, or similar)

- You stay constructive. This is a team working on consequential problems in a demanding environment. When things go sideways, you focus on the path forward. Collaboration and good energy matter

- Experience with reinforcement learning or financial derivatives is a bonus, not a baseline

- You are a quick study. Financial derivatives is a specialized domain and we do not expect you to know it coming in. What we do expect is genuine curiosity, because the depth you develop here will directly shape the quality of what you build

- MLOps or production AI experience: getting models out of notebooks and into the real world

- Cloud platform experience (Azure, AWS, or GCP)

- You are someone who moves with purpose. You form a view based on what you know, test it against reality, and adjust as you learn. Ambiguity is not a blocker for you; it is just the nature of early-stage problems

- You are pragmatic about tools and languages. You reach for whatever the project calls for and stay open to better options as they emerge

- Data engineering: designing and building pipelines that feed real workflows

- ML or data science work, especially in complex or data-constrained environments

- Building with LLMs: prompt engineering, RAG, fine-tuning, agents, or inference pipelines

- You are hands-on by preference. Writing code, building pipelines, running experiments: that is where you do your best thinking

📌 Applied AI Engineer (Toronto)
🏢 Derivative Path
📍 Toronto