Portfolio

Context. A global bank needed to replace fragmented data pipelines with a unified, auditable data platform capable of supporting regulatory reporting, analytics, and AI use cases. The existing estate was a mix of legacy ETL, ad hoc scripts, and disconnected data stores with no consistent lineage.

Approach. Designed a three-layer architecture: ingestion (Dataflow + Pub/Sub), orchestration (Cloud Composer), and modeling (dbt with Data Vault 2.0 on BigQuery). Data Vault was chosen for its insert-only pattern and mandatory metadata, enabling point-in-time reconstruction and full source traceability required by BCBS 239 and DORA.

Key decisions. CMEK encryption for all data at rest. Private IP networking with no public endpoints. Column-level security via BigQuery policy tags. Terraform modules for reproducible infrastructure. Incremental loading with merge strategy for idempotent operations.

Outcome. Unified data platform serving multiple business domains, with full lineage from source to report and regulatory examination readiness built into the architecture rather than bolted on.

Related: Data Vault 2.0 in Banking · Building Data Foundations · Why We Chose dbt

Context. A financial institution was scaling AI across credit, fraud, and customer analytics but had no consistent governance framework. Model development was ad hoc, validation inconsistent, and production monitoring fell to whoever happened to deploy the model.

Approach. Designed a tiered governance framework that scales requirements with risk: four tiers (Critical, High, Medium, Low) with distinct requirements for validation rigor, monitoring depth, and review cadence, covering the full model lifecycle from development standards to decommissioning.

Key decisions. Governance as enablement, not gatekeeping. Compliance mapping to EU AI Act (Articles 9-15) and SR 11-7 built into the structure. Worked examples (credit scoring, fraud detection) to make the framework concrete rather than theoretical.

Outcome. Consistent governance with clear accountabilities, faster time-to-production for lower-risk models, examination readiness for higher-risk ones, and a shared language between data science, risk, and compliance.

Related: ML Risk Frameworks for GenAI · LLMs Meet Compliance

Context. Enterprise AI spending is projected at $644 billion, yet 42% of companies are scrapping most initiatives. The gap is not technology. It is the absence of a management system for converting AI capability into business value.

Approach. Synthesized patterns from regulated financial services and cross-industry research into an operating framework. Five operating principles: operating model before technology, governance as infrastructure, architecture connecting intelligence to action, measurement reaching the balance sheet, and workforce designed for human-agent collaboration.

Includes. An interactive AI Readiness Assessment (25 questions, radar chart, shareable scorecard), four maturity stages, role-based reading paths for CIOs, CEOs, CAIOs, and CDOs, and a 12-month transformation roadmap.

Outcome. An open-access playbook designed to be cited in board decks and used as a decision framework by AI transformation leaders.

Read: Enterprise AI Playbook · The AI Readiness Gap

Context. Most agentic AI material teaches how to make an impressive demo. Engineers building agent systems for production need something different: precise definitions, architecture patterns that survive real constraints, evaluation harnesses, and reliability engineering.

Approach. A deep, engineering-first guide across the full stack: when to build an agent and when not to, tool design as typed contracts, context engineering, the observe-think-act loop, state and planning, evaluation with gold datasets and rubric scoring, and reliability engineering. Two threaded projects run from first principles to production readiness.

Audience. Backend, platform, and staff+ engineers, software architects, and technical leads building AI for production. Not a prompt-engineering tutorial. Not a framework crash course.

Outcome. A published book with a working Python code-companion repository, available on Amazon.

Amazon · Code companion

Context. A large enterprise needed to modernize business-critical applications from on-premises legacy infrastructure to cloud-native architecture. The estate had accumulated years of technical debt, with tightly coupled dependencies and limited observability.

Approach. Rather than a wholesale migration, adopted a phased modernization. Defined target architecture patterns per application tier, established delivery governance with clear accountability, and built shared platform capabilities (CI/CD, observability, security baseline) that teams could adopt incrementally.

Key decisions. Strangler fig over big-bang rewrite. Shared platform services over per-team duplication. Architecture decision records for every significant choice. Production readiness reviews as deployment gates rather than post-incident artifacts.

Outcome. Reduced deployment friction, improved production resilience, shared patterns across teams, and engineering capabilities that compound over time rather than reset each project.

Related: The AI Operating Model · Why Enterprise AI Programs Fail

Context. An AI-focused organization needed to move from bespoke model development to a repeatable platform for building, deploying, and monitoring NLP and deep learning models. Each project started from scratch: different pipelines, serving, and monitoring.

Approach. Built a shared ML platform with standardized components: ingestion pipelines, experiment tracking, model registry, deployment automation, and production monitoring. Event-driven architecture for real-time inference; batch pipelines for training and retraining.

Key decisions. Standardized model packaging to decouple development from deployment. Event-driven serving for latency-sensitive use cases. Automated retraining triggered by drift detection. Centralized experiment tracking for reproducibility.

Outcome. Reduced time from experiment to production, consistent monitoring across all deployed models, and reusable components that accelerated each subsequent project.

Related: NER Pipelines to LLM Agents · NLP Before Transformers · RAG in Production