Applied AI Engineer
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Job Description
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.
Requirements:
- 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.
Benefits:
- Competitive bonus, base salary, and equity compensation
- 23 days of PTO
- Fully remote
- RRSP contribution at 3%
- Competitive health benefits
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