Positioning
Applied AI / platform engineer who can turn ambiguous business workflows into tested APIs, operational runbooks, evaluation harnesses, and public-safe demos.
About / Resume snapshot
Enterprise AI builder with platform, automation, and quality-loop focus.
I build practical AI systems for document-heavy organizations: RAG/GraphRAG gateways, evaluation loops, workflow automation, reporting pipelines, and operator-facing platform dashboards.
Core stack
Python, FastAPI, Neo4j, RAG/GraphRAG, Kubernetes, PostgreSQL, automation pipelines, static portals, CI.
Strengths
Architecture, source-grounded AI, privacy-aware design, test automation, incident-ready operations, executive reporting.
Public proof
GitHub Pages portfolio plus small sanitized repositories with synthetic data, tests, CI, and no private artifacts.
Architecture map
How the public portfolio maps to enterprise AI rollout patterns
The public repositories are intentionally small, but each one demonstrates a boundary used in larger enterprise AI initiatives: controlled ask endpoints, quality evaluation, privacy guards, and operations visibility. Most internal AI work should be understood as pilots, prototypes, or controlled rollouts unless a specific live contour is named.
Selected work
What I build
The public portfolio focuses on architecture, engineering approach, and sanitized examples. Production data, internal documents, credentials, and client-specific infrastructure are intentionally excluded.
Enterprise AI gateways
Unified ask endpoints, model routing, policy checks, source attribution, and controlled LLM responses.
GraphRAG and knowledge graphs
Document-grounded search, entity resolution, organization graphs, and evidence-first answer generation.
Business automation
Meeting intelligence, daily reports, HR workflows, regulatory checks, and document processing pipelines.
AI platform operations
Kubernetes services, health dashboards, incident runbooks, static operations portals, and safe rollouts.
Public demo repository
AI RAG Gateway Demo
A compact FastAPI demo of an enterprise RAG gateway: workspace routing, synthetic retrieval, policy checks, privacy redaction, citations, tests, and GitHub Actions CI.
View codePublic demo repository
Enterprise Document AI Evaluation
A synthetic RAG/GraphRAG quality harness: capability test cases, citation recall, privacy checks, aggregate metrics, Markdown/JSON reports, pytest, and CI.
View codePublic demo repository
Meeting Intelligence Demo
A synthetic transcript-to-report pipeline: parsing, topic grouping, decisions, action items, risks, Markdown/JSON output, pytest, and CI.
View codeCase studies
Representative AI Lab projects
GraphRAGAPIPolicy
Unified Ask Endpoint
A server-side AI gateway that routes enterprise questions to the right retrieval workspace, applies source-quality and privacy policies, and returns grounded answers with traceable evidence.
- Stack
- Python, FastAPI, Neo4j, RAG, LLM routing
- Value
- One controlled entrypoint for document Q&A across multiple knowledge domains.
- Public demo
- ai-rag-gateway-demo
EvaluationRAGQuality
Enterprise GraphRAG Evaluation Loop
A repeatable evaluation framework for document-grounded AI answers: realistic query generation, regression checks, model comparisons, and failure analysis.
- Stack
- Python, benchmark runners, structured metrics
- Value
- Turns answer quality into measurable engineering feedback.
- Public demo
- enterprise-doc-ai-evaluation
Knowledge GraphDocumentsGovernance
Enterprise Knowledge Graph
A graph model for connecting documents, requirements, organizational units, roles, approval routes, and historical ownership metadata.
- Stack
- Neo4j, ontology design, ETL, entity resolution
- Value
- Provides explainable structure behind AI answers and compliance checks.
MeetingsASRReports
Meeting Intelligence Assistant
A pipeline that transforms meeting recordings and transcripts into topic-grouped summaries, decisions, action items, and review-ready reports.
- Stack
- ASR, LLM reports, workflow storage, email delivery
- Value
- Reduces manual meeting follow-up and improves auditability.
- Public demo
- meeting-intelligence-demo
OperationsKubernetesPortal
AI Operations Portal
A static operations portal that maps AI services, gateways, health checks, runbooks, incidents, and dependency chains for safer operational support.
- Stack
- Static generator, YAML registry, Kubernetes health checks
- Value
- Gives operators a single trusted map of the AI platform.
ReportsBIAutomation
Automated Daily Production Reports
A reporting pipeline that ingests operational source files, extracts structured facts, produces executive-ready summaries, and supports dashboard integration.
- Stack
- Python, parsers, PostgreSQL, BI-ready marts
- Value
- Converts manual reporting into a repeatable data product.
HR AIRubricsReports
HR Interview Assistant
A structured interview reporting assistant that maps evidence to competencies, weighted rubrics, risks, and recommendation summaries.
- Stack
- LLM reporting, competency frameworks, PDF/HTML outputs
- Value
- Improves consistency and evidence quality in candidate evaluation.
KubernetesDevOpsPlatform
Kubernetes Microservices Server
Lightweight infrastructure and operational manifests for running microservices, dashboards, and AI backends in a local/server Kubernetes environment.
- Stack
- Kubernetes, shell automation, manifests, runbooks
- Value
- Demonstrates platform ownership beyond application code.
Engineering principles
How I approach enterprise AI
Evidence first
AI answers should be grounded in traceable sources, not just fluent text.
Privacy by design
Public demos use synthetic or sanitized data. Secrets and internal artifacts stay private.
Operations matter
Dashboards, health checks, runbooks, and rollback paths are part of the product.
Quality loops
RAG quality should be measured continuously with realistic, capability-based tests.
Contact
Interested in enterprise AI platforms?
This site is a public, sanitized view of selected AI Lab work. Full production systems, client data, credentials, and internal documents are not published.