Grounded
Use trusted sources, metadata, citations, and measurable quality controls.
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.
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.
Integrated
Connect agents to APIs, databases, documents, and enterprise workflows.
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.
Agentic AI Systems
AI agents, multi-step planning, tool use, approvals, memory, evaluation, and operational controls.
- Tool-using agents
- MCP integrations
- Human approval workflows
RAG & Knowledge Systems
Document ingestion, OCR, chunking, retrieval, reranking, citations, and knowledge-access interfaces.
- Private knowledge assistants
- Document intelligence
- Vector and hybrid search
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
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
- Problem: Enterprise research and document analysis were slowed by information silos, unstructured data, and the risk of AI-generated inaccuracies.
- Solution: Engineered a multi-model, multimodal agentic RAG platform with hybrid vector retrieval, source-grounded responses, and citation tracking.
- Role: Served as CTO and executive lead, architecting the agent-orchestration engine, ingestion pipelines, and multi-tenant SaaS infrastructure from concept through deployment.
- Technology: Python, OpenAI, Claude, Gemini, Qdrant, Pinecone, hybrid semantic retrieval, FastAPI, and cloud infrastructure.
- Outcome: Launched a live AI SaaS application (aisaas.openaimp.com) with source attribution and a reported reduction of more than 70% in document synthesis and research time.
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.]]
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.]]
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 LLC
- 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.
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.
- 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.
April 2014 — August 2021
CTO
OpenAIMP LLC
- Advised executives on enterprise AI/ML strategy, data platforms, DataSecOps, and product analytics, aligning AI portfolios and architecture with business and revenue objectives.
- Led multidisciplinary data science and MLOps teams in designing multimodal data pipelines, distributed training and fine-tuning, feature and vector stores, experiment tracking, model serving, and lifecycle management.
- Architected production ML and NLP solutions on AWS SageMaker, including recommendation platforms, credit-risk models, document and contract processing, and explainable AI using techniques such as SHAP and LIME.
Education & credentials
- MBA, Finance
- MS, Computer Science
- Google Project Management
- Google AI
- AWS - Professional Cloud Architect
- SAFe for Architects
- AWS Machine Learning – Specialty
- Certified Cloud Security Professional (CCSP)
- Amazon Web Services DevOps Engineer - Professional
- TOGAF
- ITIL
- CPHIMS
- PAHM
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