class DataArchitect:
def __init__(self):
self.name = "Moji"
self.role = "Data & AI Architect"
self.location = "Milan, Italy 🇮🇹"
self.status = "Freelance Professional"
self.specialization = "Data & AI Engineering"
self.motto = "Turning Data into Intelligence"
def get_current_focus(self):
return [
"Building scalable data pipelines",
"Designing AI architectures",
"Optimizing ML workflows",
"Chess strategy optimization 🏁"
]Core strengths:
- Event-driven lakehouse governance with data contracts and quality gates
- MLOps on Azure, AWS, and hybrid GPU clusters with FinOps discipline
- Graph analytics with Neo4j and feature engineering for fraud/recsys
- Observability, SLOs, and cost-aware architecture reviews
- Event-driven lakehouse – Cut batch ETL runtime by 35% while serving 5B+ monthly events for downstream analytics.
- Multi-cloud LLM platform – Deployed resilient inference across Azure + AWS with 99.9% uptime and autoscaling GPU clusters.
- Real-time fraud signals – Built streaming graph features on Neo4j that reduced false positives by 22%.
- LLMOps control plane – Established evals + rollout guardrails to keep latency steady during 10x traffic spikes.
🎯 **Current Focus (with outcomes)**
- Accelerating ingestion and transformation to keep nightly SLAs **< 45 minutes** for enterprise BI.
- Shipping a **feature store** that cuts ML model drift by giving teams fresher signals **2x faster**.
- Hardening **MLOps** with blue/green rollouts and **p95 latency < 120ms** for customer-facing APIs.
- Coaching teams on **event-driven architectures** and **data contracts** to reduce rework by **30%**.
- Piloting **RAG for analytics** to surface trusted metrics with **<1% hallucination** in exec dashboards.
| 🎾 Tennis Enthusiast | ♟️ Chess Strategist | 🔧 Pipeline Optimizer | 🌍 Multi-Cloud Expert |
|---|---|---|---|
| Serving aces on and off the court | Always thinking 5 moves ahead | Making data flow like poetry | Architecting across all clouds |
🔥 Building the future of data architecture
🎾 Perfecting my backhand
♟️ Analyzing chess positions
☕ Fueled by espresso (it's Milan, after all!)
Chess strategy sharpens my design thinking, and tennis keeps my iteration cycles fast.
graph TD
A[🧠 AI Architecture] --> B[Data Pipeline Optimization]
A --> C[ML Model Deployment]
B --> D[Real-time Analytics]
C --> E[Scalable Solutions]
D --> F[🚀 Production Ready]
E --> F
style A fill:#1E3A8A,stroke:#3B82F6,stroke-width:2px,color:#fff
style F fill:#059669,stroke:#10B981,stroke-width:2px,color:#fff
Data Contracts in Practice – How schema governance cut rework across 20+ teams. (Link: coming soon) Scaling LLM Inference on GPUs – Patterns that kept costs predictable during traffic spikes. (Link: blog / deck) Graph Features for Fraud – Why Neo4j centrality scores boosted detection precision. (Link: notebook)
💬 "Always up for a challenge—whether it's optimizing data pipelines or finding a killer move in chess!"
📬 Availability: Open for freelance architecture sprints and technical leadership engagements. 🔗 Reach out: LinkedIn • Email
🚀 Built with passion in Milan | 🎯 Crafted for the GitHub community