Key Expertise
Experience
12+ years
Timezone
CET (UTC +1)
Skills
AI / ML
Languages
Databases
Infrastructure
Frameworks
Integrations & Protocols
Overview
Architected and developed a sophisticated loan application processing platform with agentic AI capabilities for a fintech lending company. The system combines an N8N-inspired declarative workflow engine with LLM-powered agents equipped with 30+ MCP tools for autonomous task execution. Core modules include automated bank statement extraction and fraud detection, vector-powered semantic search for document analysis, and multi-tenant architecture with complete data isolation across 76 database tables.
Achievements
The platform automated complex loan underwriting workflows, significantly reducing manual review time through configurable knockout and auto-approval thresholds. The agentic AI system autonomously handles multi-step research and verification tasks using 30+ MCP tools spanning browser automation, financial processing, and web search. A 2-pass LLM transaction classification pipeline with confidence scoring improved categorization accuracy. Multi-provider LLM architecture enables seamless switching between five providers without code changes.
Responsibilities
- Defined end-to-end system architecture: designed the workflow engine model, agentic orchestration layer, financial processing pipeline, and vector search infrastructure as cohesive modules with clear contract boundaries.
- Architected an N8N-inspired workflow engine with 15+ node types, declarative JSON-based workflow definitions, and configurable execution states.
- Designed and implemented agentic AI orchestration layer with 30+ MCP (Model Context Protocol) tools covering browser automation, business data retrieval, financial processing, and web search/deep research capabilities.
- Designed financial processing module: bank statement PDF extraction, 2-pass LLM transaction classification with confidence scoring, automated fraud detection, and configurable knockout/manual review decision logic.
- Architected vector search infrastructure with RAG pipeline, defined embedding and chunking strategies for semantic document analysis and retrieval using Weaviate.
- Designed multi-tenant data architecture with JSONB-based flexible entity storage (76 tables), event- driven audit system, and webhook notification pipeline.
- Built comprehensive observability layer: LLM metrics tracking, workflow audit trails, structured logging, and dynamic configuration management.
Technologies Used
Key Expertise
Experience
12+ years
Timezone
CET (UTC +1)
Skills
AI / ML
Languages
Databases
Infrastructure
Frameworks
Integrations & Protocols
This project was delivered by
Oleksandr S.
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