Global Director | AI Digital Transformation Expert | 20+ Years Enterprise Experience
"Debugged enterprise systems before ML was cool. Built ML when it became enterprise reality. :)"
- 2005: ASP.NET 2.0 → First production deployment at age 22
- 2012: AWS Lambda → Scaling serverless architecture before mainstream adoption
- 2018: Azure ML → Architecting enterprise ML pipelines for global scale
- 2026: AI Transformation → Solving VAT Compliance, Fraud, and BEPS Risk in Commodity Trading
Selected use cases from my private ML lab portfolio (synthetic demos).
| Business Domain | Strategic Outcome | Impact |
|---|---|---|
| VAT Compliance | Automated filing via pattern recognition | ~92% accuracy, $1.2M cost avoidance |
| BEPS & Tax Risk | Multi-jurisdictional audit readiness | ~88% precision in risk flagging |
| Invoice Integrity | Fraud anomaly detection | ~95% precision, $1.8M risk mitigation |
| Trader Analytics | Pareto-based segmentation | Top 20% clients → ~82% revenue |
| Tax Provisions | Financial forecasting | R² ≈ 0.89 reliability |
Architecture: Azure ML → SAP S/4HANA → Power BI | ISO 42001 Compliant
🔒 Access Note: Proprietary repo is private.
👉 Explore related public work here: github.com/akhileshsr
- Enterprise Stack: MS Stack (ASP.NET → Blazor → Azure), AWS (Lambda → Bedrock)
- SAP Ecosystem: ML pipelines integrated with SAP S/4HANA & Power Automate
- Global Scale: Deployed systems across 54 countries, managing $2B+ P&L data
- Recognition: CFO 100 Award for FinTax innovation
⚡ Fun fact: I still write C# faster than Python. I can deploy to Azure/AWS with my eyes closed, yet I still spend 30 minutes debugging dinner decisions.
