About the role

Applied AI Engineer developing LLM-powered features for Derivative Path's financial platform. Collaborating with domain experts to design AI capabilities in a high-stakes environment.

Responsibilities

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.

Building with LLMs: prompt engineering, RAG, fine-tuning, agents, or inference pipelines
Data engineering: designing and building pipelines that feed real workflows
ML or data science work, especially in complex or data-constrained environments
Working knowledge of Python and common AI/ML frameworks (PyTorch, HuggingFace, LangChain, or similar)
MLOps or production AI experience: getting models out of notebooks and into the real world
Cloud platform experience (Azure, AWS, or GCP)
Experience with reinforcement learning or financial derivatives is a bonus, not a baseline.