AI Engineer · IT systems integrator, Saudi Arabia (client under NDA)
Air-gapped multi-agent AI platform for public-tender (RFP) response.
- Multi-agent AI system. Architected a two-agent solution for tender response. A financial agent performs structured extraction from vendor quotations in heterogeneous formats (PDF, Word, Excel, email, supplier portals) into a costing model; a technical agent applies RAG and Arabic OCR over RFPs of hundreds of pages, extracts scope of work and bill of quantities, and drafts the technical proposal from a fixed corporate template.
- Human-in-the-loop and traceability. Engineered source-level traceability from every extracted figure back to its document and page, mandatory human review gates, and visual flagging of AI-generated content with no vendor source behind it, as required by tender confidentiality.
- LLM evaluation. Defined the evaluation harness before the build: a golden dataset of historical tenders scored on field-level precision and recall, regression testing on every prompt or model change, and OpenTelemetry traces per agent step for latency, token cost and failure analysis.
- On-premise AI infrastructure. Designed a model-agnostic architecture against OpenAI-compatible APIs, benchmarked Qwen3, DeepSeek, Falcon-H1 Arabic and ALLaM, sized GPU and VRAM for vLLM and Ollama serving, and met Saudi PDPL and NCA requirements in a fully isolated network.
Python · LangChain · LangGraph · vLLM · Ollama · Qwen3 · DeepSeek · RAG · pgvector · Arabic OCR · OpenTelemetry · Docker