NGPartners is a company focused on revolutionizing M&A diligence through an AI-driven platform. They are seeking a Financial Systems Transaction Advisory Engineer to design and build AI agents for complex financial analyses and develop a market intelligence engine, all while working in a high-autonomy environment.
Responsibilities:
- Design and build AI agents that automate complex QoE analysis, EBITDA normalization, and earnings defensibility
- Build the commercial market intelligence engine, using agentic research to map competitors and generate forward market theses
- Develop the quantitative "stress-test" layer that validates financial assumptions against macro data (FRED, sector APIs) in real time
- Architect multi-tenant pipelines to ingest and normalize data from QuickBooks, Xero, Sage, and unstructured PDFs
- Build and maintain the Val API, allowing partners to embed your intelligence layer directly into their own platforms
Requirements:
- Experience in designing and building AI agents that automate complex QoE analysis, EBITDA normalization, and earnings defensibility
- Experience in building commercial market intelligence engines using agentic research
- Experience in developing quantitative 'stress-test' layers that validate financial assumptions against macro data in real time
- Experience in architecting multi-tenant pipelines to ingest and normalize data from QuickBooks, Xero, Sage, and unstructured PDFs
- Experience in building and maintaining APIs for embedding intelligence layers into partner platforms
- Proficiency in multi-agent frameworks (LangGraph, LangChain, CrewAI), RAG optimization, and agentic memory architectures
- Proficiency in TypeScript, React/Next.js for real-time dashboards, and Python (FastAPI) or Node.js for backend development
- Proficiency in Python (NumPy, Pandas, SciPy), PostgreSQL, and ETL development for financial data aggregators
- Experience with cloud platforms (AWS/GCP/Azure), Docker, Kubernetes, and Infrastructure as Code (Terraform/Pulumi)
- Ability to design scalable architectures for multi-tenant environments
- Genuine interest in M&A deals and understanding of EBITDA normalization
- Ability to work with startup speed while maintaining accuracy
- Comfortable working in ambiguous environments and defining standards