Architecting
Agentic Intelligence.
Enterprise Architect & Digital Transformation Lead with 13 years across Business, Data, Application and Technology domains — specializing in Agentic AI systems and cloud-native architecture for Fortune 500 enterprises.
Strategy to systems — end to end.
Expert in CXO-suite advisory, target-state blueprinting and operating-model design for Fortune 500 clients across Life Sciences, Automotive, CPG and BFSI. I combine deep enterprise governance (TOGAF) with hands-on proficiency in cloud architecture, microservices and AI automation.
Proven track record of ROI-driven roadmaps unlocking $300M+ in value through intelligent digital strategies, scalable cloud transitions and operational automation — governing implementations, evaluating build-vs-buy, and enforcing strict DevSecOps guardrails across complex enterprise landscapes.
Selected Works
Agentic AI platforms and ML systems architected for global enterprises. Client names withheld; scale is real.
Agentic Customer Master Data (CMD) Platform
Event-driven multi-agent MDM automation across dual SAP ERPs — 14 specialized agents, GPT-4o classification, human-in-the-loop governance.
GenAI SCM Audit Engine
LangGraph multi-agent audit & remediation on GCP — 50–60% fewer data discrepancies.
Autonomous Maturity Assessment
6-agent LangGraph platform on NVIDIA NIM — $50M+ in influenced wins.
GxP-Compliant Clinical Supply ML Platform
Quantile XGBoost forecasting + NLP compliance rules behind a BRMS gatekeeper — 34% pilot cost reduction across 150+ trials.
Core Banking Cloud Migration
Event-driven AWS target-state for 90% of customer accounts, ~40% of national liquidity.
More Engagements
EA & Data Rationalization Strategy
Life Sciences · EUIoT & Blockchain Cold-Chain Control Tower
Life Sciences · EU/USCloud-Native IoT & Data Foundation — Greenfield EV Startup
Automotive · INLandscape Optimization & Process Mining
Automotive · IN/TRData-Driven Manufacturing Intelligence — 16-site MOM Rollout
CPG · EUAdvanced Analytics, Manufacturing to Retail
CPG · INTechnical Stack
The platforms and practices behind the architectures.
AGENTIC & GENAI
CLOUD & PLATFORM
DATA & MLOPS
GOVERNANCE & DELIVERY
EXPERIENCE LOG
Architect for an agentic AI Customer Master Data transformation at a Fortune 500 US-based global water, hygiene & sustainability leader — 14 production agents across dual SAP ERPs.
Architected GenAI and agentic platforms across Automotive, Life Sciences and BFSI — including a 2,000+ app greenfield transformation and a firm-wide autonomous assessment platform.
Digital supply chain and enterprise architecture consulting across manufacturing and consumer sectors.
Large-scale ERP, SCM and MOM/MES delivery across global multi-site rollouts.
CERTIFICATIONS
Let's build what's next.
Open to conversations on enterprise architecture, agentic AI and large-scale digital transformation.
Agentic Customer Master Data Platform
An event-driven multi-agent platform automating customer master data operations for a Fortune 500 US sustainability leader operating in 170+ countries — replacing high-volume manual data stewardship with governed, touchless AI agents across two SAP ERPs.
The Problem
Customer create, maintenance and cleansing requests arrived as free-form emails with bulk Excel attachments, manually triaged by MDM specialists into two parallel SAP ERPs (legacy ECC and S/4HANA) — slow, error-prone and impossible to scale across a 170-country footprint.
The Solution
ServiceNow AI cases with GPT-4o email classification feed Azure Event Hub category topics; Python agent apps orchestrate 14 specialized MDM agents through configurable step-loops with deduplication, enrichment, tax OCR and human-in-the-loop checkpoints — exporting validated records to both ERPs via MuleSoft.
Architecture Highlights
- LLM intake & routing: Azure OpenAI GPT-4o classifies inbound requests and transforms rows to structured JSON; ServiceNow Playbook engine stages Init → Validate → Export → Close.
- Event-driven agent fabric: dedicated Event Hub topics per request category with consumer groups per agent type; per-row parallelism handles hundreds of customers per request.
- 14 specialized agents across 2 ERPs: validation, enrichment (address standardization, naming taxonomy), 3-tier Syndigo deduplication (≥85% auto-flag), tax-certificate OCR, inactivation/reactivation and field-update agents with per-system YAML configs.
- Central LLM orchestration hub: dedicated sub-apps for agentic completion, embeddings, data integrations and operations — one governed gateway for all model traffic.
- Governed automation: Cosmos DB business rules and agent state, 26 Databricks gold materialized views, confidence-scored writeback (auto-update vs human review) and proactive tax-certificate expiry monitoring.
- Engineering rigor: shared core library — 19 modules, 504 tests, 96.96% coverage (Python, Pydantic v2) — published as a versioned internal package consumed by every agent app.
GenAI Supply Chain Audit & Agentic Automation
A LangGraph multi-agent platform on Google Cloud that transformed deterministic SAP audit error-flagging into probabilistic, context-aware remediation — part of a greenfield transformation across 2,000+ legacy applications.
The Problem
Daily SAP ABAP batch audits flagged thousands of master data discrepancies — BOM mismatches, inventory shortfalls, object dependency violations — burying Master Data Governance teams in manual triage with no engineering context.
The Solution
A gateway-first architecture exposing SAP staging tables via OData V4 through Apigee (no duplicate data lake), feeding a cyclical LangGraph state machine on GKE Autopilot that investigates each anomaly with RAG context and writes governed ServiceNow incidents.
Architecture Highlights
- Three agents, one evaluator: Retrieval (context router) → Reasoning (universal investigator) → confidence evaluator (≥0.90 threshold with rewrite loop) → Remediation (ServiceNow scribe with root-cause and routing queues).
- MCP middleware: agents act purely as MCP clients; dedicated SAP, Knowledge and ServiceNow MCP servers isolate system APIs from prompt logic — ERP changes never touch the agents.
- Dual-memory design: Vertex AI Vector Search as episodic RAG over engineering waivers, SOPs and closed tickets (suppressing documented false positives); Memorystore Redis serializes thread state.
- Asynchronous HITL: LangGraph interrupts before critical writes; SAP BTP pushes to the analyst's Fiori inbox; approval webhooks rehydrate the exact state hours later in sub-milliseconds.
- AgenticOps: prompts as code — GitHub Actions runs a LangSmith continuous-evaluation suite against a golden dataset of 100 historical SAP failures before ArgoCD may deploy; Model Armor screens all LLM traffic for PII and prompt injection.
- Dual-pane observability: OpenTelemetry/Grafana for infrastructure; LangSmith for reasoning traces, retrieval sources and token cost governance.
Autonomous Maturity-Assessment Accelerator
A zero-trust, six-agent GenAI platform that replaced manual enterprise-architecture maturity assessments with dynamic discovery, automated benchmarking and evidence-grounded roadmap generation.
The Problem
Architecture maturity assessments relied on manual discovery cycles, fragmented stakeholder interviews and static spreadsheet gap analyses — weeks of non-billable effort per engagement, with inconsistent quality.
The Solution
A hierarchical LangGraph supervisor orchestrating six specialized agents over a RAG-grounded evidence base, running on self-hosted Llama 3.1 Nemotron 70B via NVIDIA NIM — with an air-gapped MCP gateway protecting proprietary IP.
Architecture Highlights
- Supervisor pattern: an Assessment Orchestrator routes work to Evidence Extraction, Adaptive Questioning, Benchmark Mapping, Maturity Scoring and Roadmap/QA agents — the QA agent runs hallucination checks with a conditional re-scoring feedback loop.
- Sovereign model stack: Llama 3.1 Nemotron 70B on NVIDIA NIM microservices (EKS, GPU P5 instances) with NeMo Guardrails as a semantic firewall — chosen for deterministic function calling and data sovereignty.
- Zero-trust IP access: a hardened MCP gateway bridges to proprietary knowledge platforms (industry benchmarks, digital process maps, business architectures) with role-based scrubbing — agents only ever see anonymized rubrics.
- Dual-tier memory: ElastiCache Redis for active LangGraph session state; Aurora PostgreSQL for longitudinal assessment history.
- RAG evidence base: client artifacts and workshop transcripts chunked and indexed in Pinecone via AWS PrivateLink; React SPA (S3 + CloudFront) with FastAPI orchestration backend.
- AgenticOps: Terraform IaC through CodePipeline/CodeBuild; behavioral agent evals (tool-calling accuracy, retrieval precision, hallucination rate) gate every merge; LangSmith + CloudWatch + X-Ray in production.
GxP-Compliant Clinical Supply ML Platform
A zero-downtime, fully GxP-validated machine learning platform optimizing safety stock and regional drug pooling across 150+ clinical trials — where stochastic GenAI was deliberately bypassed in favor of auditable, deterministic intelligence.
The Problem
48% drug overage across the trial portfolio: static safety-stock formulas assume normal demand, which fails in skewed clinical environments — and under-forecasting risks patient stock-outs, while regulators demand full explainability and blinding integrity.
The Solution
A dual-pipeline design: quantile-regression XGBoost forecasts a 95th-percentile safety ceiling, while a clinical NLP pipeline converts protocols and regulations into structured compliance rules — reconciled by a deterministic BRMS gatekeeper before anything touches SAP.
Architecture Highlights
- Asymmetric forecasting: SparkXGBRegressor (quantile objective, α = 0.95) with a 19:1 pinball-loss penalty against under-forecasting — a stock-out costs 19× more than holding cost.
- Blinding integrity: tokenized, aggregated ingestion from SAP S/4HANA, IRT/IxRS and Veeva Vault CTMS; Unity Catalog column-level controls guarantee models never see treatment arms.
- Clinical NLP compliance: BioBERT/ClinicalBERT-based pipeline extracts country- and protocol-specific constraints ("no pooling in Germany under 90-day shelf life") into programmable rules.
- BRMS gatekeeper: a deterministic rules engine (Drools / IBM ODM) validates every ML output against extracted compliance rules — approving or overriding before dynamic reorder points and STOs post to SAP.
- GxP MLOps: SHAP explainability artifacts and MLflow lineage on every promotion; cryptographically signed 21 CFR Part 11 sign-off PDFs; shadow validation gating at ≥95% service level.
- Zero-downtime serving: SageMaker blue/green with 10% canary traffic and CloudWatch auto-rollback; K-S drift tests (p < 0.05) and a 14-day service-level floor trigger automated retraining.
Core Banking Cloud Migration & AI Data Foundation
Enterprise architecture strategy and target-state blueprinting for the bank's largest system-of-record migration — moving the legacy SAP core to AWS while safeguarding 90% of customer accounts and roughly 40% of national liquidity.
The Problem
A legacy SAP core banking platform locked customer and transaction data in silos, constraining real-time product innovation and AI adoption — with zero tolerance for data loss or downtime under APRA regulatory scrutiny.
The Solution
An event-driven AWS target-state architecture with a zero-loss migration strategy, strict APRA compliance guardrails and integration frameworks that decoupled legacy silos — establishing the resilient data foundation for real-time, AI-driven financial services.