Sr. FinOps practitioner transitioning into ML/AI engineering. I combine 10+ years in business analytics and cloud operations with a bias toward building real things and documenting them honestly. This is a working portfolio — not a finished one.
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An autonomous research agent that surfaces cloud cost data, benchmarks, and recommendations. LangGraph orchestration, Claude Haiku/Sonnet, Tavily search, deployed on AWS Fargate.
Published
Assessed my DocFlow pipeline against the NIST AI Risk Management Framework. Turns out FinOps already covered most of it — the tagging, the audit trails, the cost controls, the documented trade-offs. Here's where the overlap is real, and where the vocabulary runs out.
Complete
Built a serverless AWS document pipeline with Lambda, Textract, and Comprehend. Hit 80% success rate, documented the wall, decided to ship anyway. The engineering decisions — including the ones that didn't work — are all here.
Complete
Turned an AWS certification into a deployed full-stack project — static site on S3, visitor counter via Lambda and DynamoDB, CI/CD pipeline via GitHub Actions. The portfolio site you're on right now is the output.
Published
Built the infrastructure, then turned the lens on the costs. Analyzed real AWS Free Tier usage, deployed budget guardrails, and built tagging-based visibility from scratch.
Published
A curated framework to bring clarity, consistency, and control to scalable infrastructure. How I think about cloud governance — and why most orgs get the sequencing wrong.
Directed enterprise-wide FinOps strategy at scale — executive dashboards, unit cost KPIs, and Savings Plan utilization. Built cost allocation frameworks in partnership with IT Finance and drove showback/chargeback adoption across engineering teams.
Established cloud data governance standards integrating Cloudability, Apptio BI, and AWS native tools. Implemented Smarsh's first FinOps program from requirements through governance, reporting to the Board.
Consolidated multi-source reporting to support executive decision-making. Designed and tested data solutions in Salesforce Lightning, Apptio BI, and Power BI, serving as the key liaison between business units and engineering.
Skilled in AWS cost modeling, SQL, and Excel-based forecasting. Experienced in SaaS data integration, cost tagging, and usage-based chargeback automation. Strong foundation in data validation, QA, and observability metrics.
Led cross-functional initiative with Infrastructure & Reliability and InfoSec Governance teams to redefine measurable SLAs — reducing operational risk by 63% and strengthening enterprise resilience.
Transformed raw IT data into actionable insights for RCA and risk prioritization workflows, driving measurable improvements in uptime, reliability, and compliance.
Building serverless ML pipelines on AWS with Lambda, Textract, and Comprehend. Designing agentic AI systems using LangGraph, Claude, and Tavily — deployed on Fargate.
Interests: AI Governance, data engineering, IAM, and cloud-native architecture. Everything gets documented and shipped. A working portfolio — not a finished one.
