About the role

Applied AI Engineer building LLM-powered features and agentic workflows for Derivative Path’s cloud platform serving financial institutions. Owning data pipelines, retrieval architectures, and production AI delivery.

Responsibilities

Build and ship LLM-powered features across the DerivativeEDGE platform.
Translate complex financial workflows into reliable AI capabilities with domain experts.
Design and implement agentic workflows that reason, act, and recover across multi-step production processes.
Own the data engineering layer supporting AI systems, including pipelines, retrieval architectures, context design, and data quality.
Prototype quickly, evaluate solutions, and determine when they are ready for production.
Contribute to the AI Lab’s technical direction, evaluations, tooling, MLOps practices, and reusable engineering patterns.
Work on NLP, model fine-tuning, synthetic data, or reinforcement learning as projects require.
Collaborate with Engineering, Product, and domain experts.
Report to the AI Lab lead.

Experience building with LLMs, including prompt engineering, RAG, fine-tuning, agents, or inference pipelines.
Experience designing and building data pipelines that feed real workflows.
ML or data science experience, especially in complex or data-constrained environments.
Working knowledge of Python and common AI/ML frameworks such as PyTorch, HuggingFace, and LangChain.
MLOps or production AI experience, including moving models from notebooks into production.
Experience with a cloud platform: Azure, AWS, or GCP.
Curiosity about and willingness to learn the financial derivatives domain.
Pragmatic approach to tools and programming languages.
Experience with reinforcement learning or financial derivatives is a bonus, not a baseline.
Must be legally entitled to work in Canada or address Canadian work-visa sponsorship requirements.