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Vessel Sanctions Risk Assessment System

AI EngineerAndrii B.
Andrii B.
Andrii B.

Senior AI Engineer

LLM & AI Agents

Key Expertise

Multi-Agent SystemsConversational AILLM IntegrationSystem ArchitectureContext Compression

Experience

10+ years

Timezone

CET (UTC +1)

Skills

AI / ML

Azure AI FoundryContradiction ResolutionMemory layerCustom multi-agent orchestrationGoogle AI (intent recognition)OCRCustom context compressionAnthropic ClaudeAnthropic Claude (Sonnet / Opus)

Languages

PythonRust

Databases

Azure Blob Storage

Infrastructure

AzureAzure DevOpsDockerCI/CD

Frameworks

FastAPIBot frameworks

Integrations & Protocols

SAP IntegrationMicrosoft Teams APIERP IntegrationManaged IdentityAzure ADPayment APIsGDPR & EU AI Act complianceOpenText
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Overview

A mission-critical regulatory compliance system for automated EU sanctions screening of maritime vessel voyages at Metinvest Group — a $12B+ enterprise with global shipping operations. The system replaced a manual, error-prone compliance review process with a fully automated, auditable pipeline capable of processing hundreds of voyages annually. Regulatory defensibility was a hard requirement: every automated decision had to be traceable, explainable, and audit-ready.

Achievements

• Automated screening of hundreds of vessel voyages per year — compliance team shifted from full document review to exception handling only. • Two-layer architecture (rule-based screening + LLM for ambiguous cases) reduced false positives while maintaining zero-miss accuracy on sanctioned entities. • Full audit trail on every decision ensured regulatory defensibility — zero findings during external compliance audits. • End-to-end pipeline from SAP trigger to auto-decision writeback achieved without any manual handoffs for clear-cut cases.

Responsibilities

  • Designed the complete pipeline architecture: SAP (IMO trigger) → Azure Blob Storage → PDF ingestion → OCR extraction → rule-based EU sanctions screening → LLM scoring for ambiguous cases → auto-decision or Teams escalation (human-in-the-loop) → writeback to SAP.
  • Implemented a two-layer compliance engine combining deterministic rule-based logic for clear cases with LLM-based probabilistic scoring for ambiguous vessel records.
  • Built a full audit trail subsystem ensuring every decision — automated or escalated — was logged with decision rationale for regulatory review.
  • Integrated with Azure Blob Storage (Managed Identity), SAP, and Microsoft Teams API for human-in-the-loop escalation workflows.
  • Engineered the system for zero third-party data exposure — all inference ran on self-hosted infrastructure within the corporate perimeter.

Technologies Used

FastAPIPythonAzure Blob StorageManaged IdentityOCRAzure AI FoundrySAP IntegrationMicrosoft Teams APIDockerCI/CDAzure DevOps
Andrii B.

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Andrii B.

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