About the role
Responsibilities:
- Build the AI agents at the heart of the product's multi-step reasoning over a user's finances, planning, and executing financial workflows.
- Build retrieval and grounding over structured and unstructured financial data, so answers are accurate, current, and specific to the individual.
- Design the evaluation harness, guardrails, and observability measuring quality rigorously, where being wrong carries real cost.
- Build the conversational experience, including voice, so talking to Corpus feels natural and fast.
- Build backend services, APIs, and data pipelines that take these systems from prototype to production.
- Stay ahead of a fast-moving model landscape and bring in what's worth adopting and discard what isn't.
Requirements:
- 3-8 years building software, with depth in applied AI/ML, backend systems, or LLM applications.
- Strong Python and sound engineering fundamentals you write code others can build on.
- Experience taking LLM-powered systems to production: RAG, agents, and the unglamorous work of making them reliable.
- Be serious about evaluating; you measure model quality with rigor and let data drive decisions.
- Fluency with embeddings, vector stores, and orchestration frameworks and the judgement to know when not to use them.
- Fast, iterative operator: you ship, watch how real users respond, and improve quickly.
Bonus:
- Voice and speech models, or real-time conversational systems.
- Agent frameworks: LangGraph, LlamaIndex, CrewAI, or similar.
- Production vector stores (Pinecone, Weaviate, Qdrant, pgvector) and AI observability tooling.