We are building an Agentic AI platform to transform credit origination by automating manual bottlenecks and enabling lenders to underwrite more loans with greater accuracy and lower risk.

We are focused on building AI systems that can perform deep, multi-step reasoning across complex financial data, legal documents, and market contexts.

Role :

We are looking for an Applied AI Engineer to design and build production-grade Agentic AI systems for credit analysis.

You will work at the intersection of LLMs, AI agents, reasoning systems, data engineering, and financial technology.

Key Responsibilities :

- Design and implement stateful, multi-agent pipelines for complex credit analysis.

- Develop advanced inference and prompt optimization pipelines using frameworks such as DSPy and GEPA.

- Experiment with reasoning techniques including Chain-of-Thought, Tree-of-Thought, and Graph-of-Thoughts.

- Build rigorous AI evaluation systems using LLM-as-a-Judge and scenario-based testing.

- Design memory, retrieval, orchestration, and human-in-the-loop workflows.

- Build RAG and tool-integrated AI systems for financial and legal documents.

- Implement complete AI observability and tracing, including agent actions, tool calls, and intermediate states.

- Develop ETL/data engineering pipelines for structured, unstructured, vector, and graph data.

- Build dashboards to monitor cost, latency, correctness, reliability, and concept drift.

- Deploy and maintain AI services on AWS/Azure.

- Integrate AI services into modern DevOps and CI/CD pipelines.

Required Skills :

- 5+ years of professional software development experience in Python or JavaScript/TypeScript.

- Experience building and deploying production-grade Agentic AI or complex reasoning systems.

- Strong knowledge of LLMs, prompt engineering, RAG, AI agents, memory, and tool integration.

- Hands-on experience with LangChain or similar LLM/agent frameworks.

- Experience with LLM evaluation and observability tools such as Langfuse, Weights & Biases, or Helicone.

- Strong data engineering experience with ETL, SQL/NoSQL, vector databases, and data pipelines.

- Understanding of multi-agent architectures and MCP.

- Experience with AWS and/or Azure.

- Strong knowledge of Docker, CI/CD, and infrastructure-as-code.

- Understanding of AI system monitoring, evaluation, debugging, and production observability.

Nice to Have :

- Experience with RLHF/RLAIF.

- Experience with model fine-tuning using LoRA/QLoRA.

- Experience with graph databases, knowledge graphs, or Graph RAG.

- Experience in FinTech, credit risk, lending, or financial document analysis.

- Experience working with legal or financial datasets.

Why Join Us :

- Work on challenging Agentic AI and reasoning problems, rather than simple chatbot applications.

- Build foundational AI infrastructure from the ground up.

- Work on AI systems that directly impact high-stakes financial decisions.

- High ownership and significant influence over architecture and engineering practices.

- Collaborate with an experienced, product-focused founding team and strong engineering talent.