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KinSushi/README.md
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Enzo · KinSushi

Data / DataOps / MLOps / AI Engineer — Switzerland

Python · SQL · PostgreSQL · Linux · Git · Bash · Docker · CI/CD · Data Quality · Monitoring · MLflow · AI Governance

LinkedIn Location Focus

In one line

I build reliable data and ML systems for regulated, data-intensive Swiss environments: ingestion, SQL, data quality, monitoring, MLflow, Docker, CI/CD, technical documentation and AI governance — all demonstrated with synthetic data only, no client or employer data.

Start here — three repositories that show what I do

Repository What it proves Stack
banking-dataops-monitoring A complete DataOps control loop: SQL controls, reconciliation, dashboard, incident runbooks. CI + tests. PostgreSQL · Python · Streamlit · DataOps
fraud-mlops-control-tower A synthetic ML lifecycle: threshold tuning, model serving, monitoring and governance docs. scikit-learn · MLflow · FastAPI · Docker
swiss-data-ai-engineering-lab A reproducible engineering-literacy lab: data quality, format validation, public-safety scanning. Python · SQL · MLOps · Governance

Every repository runs on synthetic or open data only. CVs, cover letters, salary targets and employer-specific notes are kept off public GitHub on purpose.

More technical evidence

Repository Role
database-migration-quality-lab Legacy-to-target migration: validation, reconciliation, rollback planning
secure-wealth-rag-assistant Secure RAG / LLMOps with privacy controls and retrieval evaluation
jedha-rncp35288-portfolio Sanitized RNCP35288 evidence portfolio (six-block structure)
pty-flights-pricing Production-style Python API automation pipeline with scheduling and alerting
sovralys-infra-lab Linux / DevOps infrastructure lab: KVM, Tailscale, Docker, runbooks
git-workflow-demo Professional Git workflow reference: SSH, branching, conventional commits
OpenClassroomsProject Web development delivery evidence (RNCP38145)

What I can do for a Swiss team

  • Banking / insurance / fintech — SQL, controls, auditability, data quality, monitoring, MLOps, governance.
  • Pharma / life sciences — traceability, validated pipelines, documentation discipline, lineage.
  • Retail / e-commerce / industry — events, APIs, analytics, anomaly monitoring.
  • Cloud / platform teams — Docker, CI/CD, observability, reproducible data systems.

Target roles: Data Engineer · DataOps Engineer · MLOps Engineer · Data Quality / Risk Analyst · Application & Data Support Engineer.

Training & status

Track Status
Jedha Data Science & AI — RNCP Level 6 / 7 In progress, 2026
OpenClassrooms — Web Developer / RNCP38145 Active
Switzerland data transition Active — Geneva, Lausanne, Zurich, Basel

Languages

Language Level
French Native
Italian Native
English Professional — C1
Spanish Operational — B2
German In Progress — B2

Open to Data / DataOps / MLOps / AI roles in Switzerland. Reach me on LinkedIn

Public GitHub optimized for technical evidence. Application materials and private documents are intentionally kept off this profile.

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  1. banking-dataops-monitoring banking-dataops-monitoring Public

    Regulated-data monitoring lab with SQL controls, PostgreSQL checks, reconciliation, dashboards and incident runbooks.

    Python

  2. database-migration-quality-lab database-migration-quality-lab Public

    Legacy-to-target database migration lab with SQL validation, reconciliation, rollback planning and data-quality controls.

    Python

  3. fraud-mlops-control-tower fraud-mlops-control-tower Public

    Synthetic fraud/risk MLOps lab with MLflow, FastAPI, Docker, threshold tuning, model monitoring and governance docs.

    Python

  4. jedha-rncp35288-portfolio jedha-rncp35288-portfolio Public

    Public sanitized six-block evidence portfolio for the Jedha Data Science & AI RNCP track.

  5. secure-wealth-rag-assistant secure-wealth-rag-assistant Public

    Secure RAG and LLMOps lab with synthetic wealth documents, retrieval evaluation, privacy controls and AI governance.

    Python

  6. swiss-data-ai-engineering-lab swiss-data-ai-engineering-lab Public

    Universal Swiss Data & AI engineering lab covering languages, file formats, data systems, MLOps, observability and governance.

    Python