Opportunity
Join us to tackle one of the most complex problems at the intersection of AI and finance: creating systems that perform deep, multi-step reasoning over complex financial data, legal documents, and market contexts.
This role is about moving beyond pattern matching to build models that truly understand and analyze credit risk.
Responsibilities
• Design and implement stateful, multi-agent pipelines that are capable of performing complex credit analysis.
• Advanced inference & prompt optimization – develop and optimize prompt chains and pipelines using frameworks like DSPy and GEPA to programmatically manage and tune reasoning steps, moving beyond brittle, hand-crafted prompts.
• Implement and experiment with techniques such as Chain-of-Thought, Tree-of-Thought, and Graph-of-Thoughts to enhance reasoning capabilities.
• Create a rigorous evaluation system using LLM-as-a-judge and scenario-based testing to
measure accuracy, robustness, and reasoning quality.
• Architect memory management, retrieval, and orchestration strategies to support multi-agent workflows with human-in-the-loop review.
• Instrument our AI systems for complete traceability and observability, logging all agent actions, tool calls, and intermediate reasoning steps for debugging, audit, and compliance.
• Develop ETL pipelines and data engineering workflows to handle structured, unstructured, vector, and graph data.
• Build dashboards to track key metrics: cost, latency, correctness, and concept drift.
• Maintain AI services on cloud environments (AWS, Azure) and integrate them into broader DevOps pipelines.
Qualifications
• 5+ years of commercial development experience in Python or JS.
• Demonstrated experience building and deploying production-level Agentic AI or complex
reasoning systems.
• Deep expertise in the modern LLM Ops stack: You have hands-on experience with frameworks such as LangChain and evaluation tools (Langfuse, W&B, Helicone).
• Strong background in data engineering: ETL processes, SQL/NoSQL databases, vector
databases, and graph data models.
• Deep understanding of AI agent architectures: prompt engineering, RAG, memory, HITL, tool integration, and multi-agent control (MCP).
• Proficiency with cloud platforms (AWS, Azure) and modern DevOps practices (CI/CD,
containerization, infrastructure as code).
Nice-to-have:
• Direct experience with RLHF/RLAIF pipelines or model fine-tuning (LoRA, QLoRA).
• Experience with graph data models.
Why Join Us?
• Solve the Hard Problems: You will be working on the frontier of applied reasoning AI, not just another chatbot. Your work will have a direct impact on high-stakes financial decisions.
• Build the Foundational Stack: You won’t be plugging together APIs. You’ll be designing the core evaluation, observability, and reasoning architecture from the ground up.
• Unprecedented Impact & Ownership: As an early engineer, you will have a significant voice in our technology, architecture, and culture.
• Work with a World-Class Team: Collaborate with a logical, product-obsessed founding team and engineers from top-tier backgrounds.
