MGCA / Selected professional experience

Complex systems.
Considered decisions.

Experience across legal services, public services and research, connecting architecture with the realities of enterprise delivery.

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Sculptural emerald glass and platinum connected
Business context. Technical depth.

Selected examples from Alexander Marinov’s professional experience. Illustrations are conceptual; summaries omit confidential details and do not imply client endorsement.

Conceptual legal work illustration
01 / Legal services

Legal services

Controlled document intelligence

Architecture for document review, contract analysis and secure e-discovery.

Context & contribution

The context

Document-intensive teams need useful automation while protecting sensitive information and preserving review responsibilities.

Professional contribution

Architecture work involving LLM-assisted document review and contract analysis, secure e-discovery ingestion and the interfaces between business systems.

Architecture considerations

Identity, encryption, retention, data handling and the boundary between AI assistance and authorised action.

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Conceptual public work illustration
02 / Public services

Public services

Connected citizen services

Architecture and delivery support for a citizen recommendation service.

Context & contribution

The context

A citizen-facing service needs to connect user needs with reliable content and services while meeting public-service expectations.

Professional contribution

Architecture and delivery work on a recommendation engine using decoupled services and Azure-based integration and data components.

Architecture considerations

Service boundaries, accessibility, security testing and the operational requirements of a public-facing service.

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Conceptual research work illustration
03 / Research & life sciences

Research & life sciences

Research data to ML workflows

Architecture connecting research repositories with cloud machine-learning workflows.

Context & contribution

The context

Research teams need to move data between repositories and cloud platforms without losing sight of repeatability and operational ownership.

Professional contribution

Architecture work spanning research data repositories, Azure and AWS machine-learning platforms, and batch and real-time data flows.

Architecture considerations

Data movement, pipeline orchestration and the handover from experimentation to repeatable ML operations.

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