Founding AI Engineer

You'll work directly with the founders on the AI that sits at the core of Corpus: the agents, reasoning, retrieval, and voice that turn someone's financial life into advice they can trust and act on. This isn't a wrapper on top of an LLM; it's a system that reasons over portfolios, plans across steps, and executes real financial workflows. You'll own these systems end to end.
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.
Expectations:
This is a high-ownership, in-person role in Bangalore. The work is research-adjacent but production-oriented; you'll prototype an approach in the morning and deploy it by evening. This works well for someone who's excited by frontier AI applied to a real, high-stakes domain. It may not be the right fit if you want to focus purely on research without shipping.
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.

Experience
3-7 yrs