I architect and lead the development of Enterprise AI platforms that transform how organizations make decisions, automate operations, and unlock business intelligence across global markets.

Over the past years, I've designed and delivered cloud-native AI, Machine Learning, and Generative AI solutions across AWS and GCP, evolving from building production ML systems to leading enterprise-scale AI platforms that combine Agentic AI, Multi-Agent Systems, Enterprise Memory, RAG, and Robust AI Governance.

Functioning as an AI Platform Lead, I drive the end-to-end lifecycle of enterprise AI initiatives, from customer discovery and solution architecture to technical leadership and production delivery. I work closely with business leaders, product teams, and engineering organizations across the United States (US), European Union (EU), and South Asian markets to translate complex business challenges into scalable, secure, and measurable AI solutions.

My recent work focuses on building Enterprise AI Intelligence Layers that unify Sales, Service, Finance, and enterprise knowledge into a single AI-native platform. These platforms enable conversational business intelligence, cross-functional reasoning, autonomous workflows, persistent enterprise memory, and executive decision support, moving beyond traditional dashboards toward intelligent, AI-driven enterprise operations.

I enjoy designing systems where specialized AI agents collaborate, reason across multiple business domains, and orchestrate enterprise workflows while maintaining rigorous security, multi-jurisdictional governance, data residency, and observability at scale.

• Enterprise AI Architecture & Intelligence Layers
• Agentic AI & Multi-Agent Systems
• Global AI Governance, Security & Compliance (EU AI Act, GDPR, NIST AI RMF, HIPAA, DPDP Act)
• Multi-Jurisdictional AI Deployments (United States, European Union & South Asia)
• Large Language Models (LLMs) & Generative AI Systems
• Enterprise Memory & Retrieval-Augmented Generation (RAG)
• Model Context Protocol (MCP) & Agent Security Guardrails
• Cloud AI Architecture & MLOps (AWS & GCP)
• Technical Leadership & Solution Architecture

Navigating complex regulatory environments is central to architecting enterprise AI. I design architectures that embed compliance, privacy-preserving AI, and strict guardrails by design across multi-national regulatory landscapes:

• 🇪🇺 European Union (EU Market — EU AI Act & GDPR):
  Architecting AI systems aligned with the EU AI Act compliance tiers (prohibited, high-risk, GPAI models) and GDPR. Implementing automated provenance tracking, model explainability, algorithmic audit logs, zero-retention data pipelines, and strict sovereign data residency controls.

• 🇺🇸 United States (US Market — NIST AI RMF, HIPAA, SOC 2):
  Deploying enterprise AI aligned with the NIST AI Risk Management Framework (AI RMF), HIPAA (for clinical & healthcare AI), and SOC 2 Type II. Engineering automated PII/PHI redaction, role-based access control (RBAC), prompt injection defenses, and zero-trust agent authorization layers.

• 🌏 South Asian Markets (DPDP Act & Regional Data Localization):
  Leading regional AI adoption tailored to national data protection mandates (such as India's Digital Personal Data Protection Act - DPDP). Structuring sovereign hybrid-cloud deployments, local compute residency, and cross-border data transfer safeguards that respect regional regulatory policies.

Unified Cross-Functional Reasoning & Enterprise Memory
Architected a collaborative multi-agent platform for enterprise intelligence, unifying Sales, Service, and Finance domains. Orchestrates specialized agents (SQL Planner, Executioner, Data Critic, and Visualizer) to securely query, validate, and analyze multi-source enterprise data with zero SQL-injection risk and persistent memory.

Secure Control Plane for Autonomous Agents
Designed an enterprise-grade secure control plane for agent-tool interactions, implementing role-based access control (RBAC), Model Context Protocol (MCP) policy enforcement, transaction telemetry, and guardrails to prevent agent hallucination, data leakage, and regulatory non-compliance across US, EU, and South Asian operations.

AWS GPU Batch Inference & HIPAA Compliance
Engineered a medical image processing pipeline using AWS Batch and GPU orchestration (MRIQC + FastSurfer) to automate radiology workflows, reducing processing latency by ~60% while maintaining strict clinical compliance and PHI de-identification.

Multi-Agent Threat Intelligence & Compliance
Built a secure multi-agent reasoning network for automated threat intelligence and compliance analysis, combining secure document ingestion with strict multi-agent verification control loops.

Deep Learning for Clinical Decision Support

• IVF Embryo Grading: Developed a multi-head DenseNet model for structured embryo scoring.
• Lung Opacity Segmentation: Built an attention-based U-Net model for automated pneumonia detection on CXRs.

Beyond technology, I'm passionate about building high-performing global engineering teams, mentoring engineers, collaborating with international stakeholders, and delivering AI solutions that create measurable business value with uncompromising security and ethics.

I believe the future of enterprise software lies in AI-native platforms that don't just answer questions—they reason, learn, uphold regulatory trust, and help organizations make better decisions.

• 🏆 Top 2% — Kaggle Brain Tumor Radiogenomic Classification Challenge
• 📄 IEEE Publication — Electrical Field Distortion Around Man-Made Objects

🚀 Building scalable, reliable, and governed enterprise AI systems for real-world impact.