AVAILABLE FOR SELECT PROJECTS

Building practical agentic AI systems for complex work.

I design reliable AI, data, and software systems that connect knowledge, tools, workflows, and human judgment. My work spans agentic applications, RAG, data platforms, integration architecture, and operational automation.

Focus AI systems & automation
Location New York, United States
Engagement Architecture · Build · Advisory
01

Agentic AI &
tool-using systems

02

RAG & document
intelligence

03

Data platforms &
analytics engineering

04

Integration, DevOps &
reliable automation

01 / ABOUT

Systems thinking for AI that must work in the real world.

I work at the intersection of AI, data, software architecture, and operational delivery. I help turn unstructured information, fragmented systems, and manual processes into auditable, useful workflows.

My approach prioritizes explicit requirements, secure integration boundaries, source-grounded outputs, observability, and human review for consequential actions. The goal is not AI theater—it is a maintainable system that improves how people research, decide, build, and operate.

01

Grounded

Use trusted sources, metadata, citations, and measurable quality controls.

02

Integrated

Connect agents to APIs, databases, documents, and enterprise workflows.

03

Governed

Build permissioning, approval gates, logging, evaluation, and fallback paths.

02 / EXPERTISE

Capabilities across the full delivery lifecycle.

From strategy and architecture through prototyping, implementation, documentation, and operational improvement.

A1

Agentic AI Systems

AI agents, multi-step planning, tool use, approvals, memory, evaluation, and operational controls.

  • Tool-using agents
  • MCP integrations
  • Human approval workflows
A2

RAG & Knowledge Systems

Document ingestion, OCR, chunking, retrieval, reranking, citations, and knowledge-access interfaces.

  • Private knowledge assistants
  • Document intelligence
  • Vector and hybrid search
A3

Data & Analytics Engineering

Data pipelines, semantic modeling, SQL, analytics workflows, data quality, and decision-support systems.

  • ETL / ELT pipelines
  • Analytics and reporting
  • Data modeling and governance
A4

Platforms & Integration

API architecture, Docker environments, workflow orchestration, observability, and enterprise integration.

  • REST APIs and webhooks
  • Docker and Linux
  • CI/CD and operational reliability

03 / SELECTED WORK

Representative project areas.

Replace these placeholders with projects you are permitted to describe publicly. Remove confidential names, data, client details, and internal metrics.

01

AGENTIC AI / KNOWLEDGE OPERATIONS

Source-grounded research and document-intelligence workflow

[[Replace with a concise project summary: the business problem, the system architecture, your role, the tools used, and the outcome.]]

Python RAG Vector Search APIs Human Review
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02

DATA ENGINEERING / ANALYTICS

Integrated analytics and semantic-data platform

[[Replace with a concise project summary: data sources, pipeline design, semantic layer, governance controls, dashboards, and measurable impact.]]

SQL Python Data Modeling Dashboards Data Quality
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03

AUTOMATION / PLATFORM RELIABILITY

Containerized workflow platform and operational control plane

[[Replace with a concise project summary: workflow automation, container architecture, integrations, monitoring, approvals, and operational outcomes.]]

Docker Linux APIs Observability Automation
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04 / RESUME

Experience, technical depth, and delivery focus.

Download full resume

Professional summary

Enterprise AI and technology leader with experience owning the AI agenda from strategy and investment through talent, infrastructure, governance, and enterprise adoption. Builds agentic AI capabilities—including multi-agent workflows, tool integration, and retrieval-augmented knowledge systems—designed for secure, regulated environments and measurable business value. Has led technology organizations spanning AI, data, applications, infrastructure, and cybersecurity, including service as interim CISO and leadership of technology diligence and integration during M&A. Partners with CEOs, boards, and executive teams to translate business priorities into architecture, operating models, and delivery plans across healthcare, financial services, manufacturing, consulting, startups, and the public sector.

Core technical skills

AI & Data Python, RAG, LLMs, MCP, embeddings, vector search, SQL, analytics engineering
Architecture APIs, integration, workflow design, enterprise systems, governance, documentation
Platform & DevOps Docker, Docker Compose, Linux, CI/CD, observability, cloud-native patterns

Experience

November 2022 — Present

CAIO

OpenAIMP

  • Led enterprise AI strategy, roadmaps, and architecture governance, partnering with executives and product leaders to turn business priorities into responsible, production-ready AI capabilities.
  • Architected agentic AI and RAG/GraphRAG solutions, including multi-agent, event-driven, long-running workflows with model, tool, API, and enterprise-data integration.
  • Scaled AI delivery through data, training, inference, and CI/CD pipelines while establishing evaluation, observability, security, privacy, and model-lifecycle controls.
[[YYYY]] — [[YYYY]]

[[TITLE OR ROLE]]

[[COMPANY]]

[[Add 1–3 concise, outcome-oriented bullets or sentences about responsibilities and results.]]

August 2021 — November 2022

Chief Architect

Snap Finance Inc.

  • Led enterprise architecture for a global IT organization supporting lending, leasing, banking, and credit card businesses on AWS; advised the EVP Global CTO on technology strategy, governance, and investment.
  • Supported the company's first M&A integration and conducted architecture and technical due diligence, assessing infrastructure, product and intellectual-property risks, and strategic fit.
  • Advanced digital transformation by migrating legacy capabilities toward micro-frontends, microservices, and cloud-native MLOps and DevOps environments; championed rationalization initiatives to reduce operating and maintenance costs.
  • Guided 15+ concurrent infrastructure, architecture, data, and AI/ML projects; managed vendor relationships and a $5M global IT infrastructure budget; and oversaw incident management for 50+ monitored event-driven microservices and APIs.
  • Partnered with AI/ML and data science teams on real-time risk scoring, customer-journey personalization, and AI-driven data-loss prevention. Elevated analytics opportunities that identified $2M+ in potential savings and helped establish the supporting strategy and infrastructure.
  • Strengthened cross-functional governance through architecture, information-security, data-governance, technology-steering, and risk committees while guiding build-versus-buy and managed-service decisions.

Education & credentials

[[Add degree(s), institution(s), relevant certifications, or professional development.]]

05 / CONTACT

Have a complex workflow, data problem, or AI system to build?

I am available for architecture advisory, technical discovery, implementation planning, prototype development, and systems-integration engagements.

services@openaimp.com