Currently focused on AI Engineering and emerging technology. I'm driven by purpose, creativity, and a desire to push myself into experiences that expand how I see the world.
I'm at my best when I'm learning fast, keeping my imagination sharp, building something impactful, and moving toward the edge of what feels comfortable.
Connect With Me ↓
About
I'm a Senior Solutions Engineer at CloudZero, where I help large companies understand and control what they spend on cloud and AI. Day to day that means sitting with engineering, finance, and AI platform teams as they work to visualize and understand how Claude, OpenAI, Cursor, Copilot, and other AI usage applies back to the people and products driving it, alongside the outcomes they're delivering. Six years of technical pre-sales at VMware, Broadcom, and CloudZero taught me how to earn trust with engineers, translate complexity for executives, and close competitive deals. More recently I've learned the hardest problems in enterprise AI sit outside the models, in trustworthy data, proper context, and numbers people will actually act on.
The pattern in my best work is the same whether I'm selling or building: get into the customer's real data fast, be honest about what works and what doesn't, and ship something they can use this week. That approach has cleared compliance blockers other vendors walked away from, turned AI cost visibility into a reason for executives to buy, and cut our evaluation cycle in half. I'm looking for a role where I can do more of exactly that, embedded with customers at a company building the AI frontier.
Interests
01 Travel
02 Health & Wellness
03 AI & Technology
04 Photography & Videography
05 Animal Welfare
Education
University of New Hampshire
2014 – 2018
Bachelor of Business and Communications
Men's D1 Rugby
Resume
CloudZero
Senior Solutions Engineer ↑ Feb 2026
Sep 2024 – Present
Technical pre-sales at a cloud cost intelligence platform managing $14B+ in customer cloud spend. Run POCs, deliver technical demos, and help engineering and finance teams build cost allocation models across AWS, GCP, and Azure.
Broadcom · Tanzu
Senior Solutions Engineer
Dec 2023 – May 2024
Cloud management, security, and application observability post-VMware acquisition. Maintained technical relationships and pipeline during a complex org transition.
VMware · CloudHealth & Secure Clouds
Senior Solutions Engineer
Aug 2021 – Dec 2023
Led technical evaluations for VMware's cloud cost management and security platforms. Built the Technical Enablement Program, closed the largest CloudHealth commercial deal, and maintained 90%+ customer renewal rates.
VMware · CloudHealth
Technical Account Manager
Apr 2020 – Aug 2021
Owned post-sale success for 100+ customers. Built onboarding programs and automated outreach workflows that drove consistent retention above 90%.
VMware · CloudHealth
Inside Sales Representative
Jan 2019 – Apr 2020
Grew pipeline through prospecting and discovery. Entry point into cloud cost management and the SE track.
Highlights
01
$900K TCV Across Three Clouds, GovCloud, and AI Cyber Resilience Platform
An 11-month proof of value on a ~$120M estate spanning AWS, AWS GovCloud, Azure (~200 subscriptions), Azure GovCloud, and GCP, closed at $900K TCV. The deal stalled under a new CFO until AI cost allocation across Claude, Copilot, Gemini, and GPT turned it into a leadership priority, and the customer signed on as an AI design partner.
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- Turned AI into the reason to buy. Demonstrated per-user, per-model, and per-department cost for Claude, Copilot, ChatGPT, Gemini, Cursor, Bedrock, and Vertex AI to a data team hand-building the same pipelines. They became the champions who pushed procurement to sign early.
- Cleared the FedRAMP blocker in week three. Wrote the script that mapped Azure GovCloud billing into CloudZero's ingestion format, proving compliant coverage before the customer had to build anything.
- Automated a full-time manual process. Built the dimensions and telemetry splits that allocate 11 AWS billing organizations of shared spend back to customers, replacing a monthly pipeline one engineer ran by hand.
- Found real money fast. ~$200K/year in orphaned KMS keys, a $67K storage spike, and a $44K database anomaly, all surfaced within days of connecting data.
- Gave the CIO board-ready numbers. Built the drill-downs and reservation coverage views used to negotiate $500M+ in five-year cloud commitments.
- Built champions in six teams. Cloud ops, finance, procurement, engineering, the AI data team, and the CIO office each had their own reason to want the platform, so the deal survived a CFO change and a months-long stall.
02
Demo to Close in 28 Days Digital Asset Infrastructure
Won over a staff infrastructure engineer with deep billing-data expertise on a ~$7–8M AWS estate with 8 Kubernetes clusters: validating node-level cost math against double-counting, working network-attribution edge cases, and bringing ~$30K/month of CI spend into the model through custom ingestion. Being straight about what the product didn't do yet became the trust accelerant.
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- Scoped a proof-of-concept spanning AWS, Snowflake, 8 EKS clusters, and CI-pipeline spend brought in through CloudZero's bring-your-own-data ingestion.
- Walked the evaluator through the Kubernetes cost algorithm, max(request, usage) per pod, and proved out no node-level double-counting, plus PVC/EBS reconciliation and reservation pass-through.
- Worked a hard edge case: attributing network egress for blockchain validator traffic at flow-level granularity.
- Resolved an SSO configuration lockout before the final close call, and built engineering dashboards cost-ranked to stay inside the BI layer's row limits.
03
$475K Identified, $175K Actioned Before Signature Supply-Chain SaaS
A 4-month competitive evaluation on an $11–13M AWS estate: 15 technical sessions, 20+ stakeholders, and a mid-engagement champion departure. Built the allocation model for the ~25% of spend that was shared infrastructure and turned the technical work into a CFO-ready business case. The customer's engineers actioned $175K of identified savings before the contract was signed.
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- Scoped the trial around explicit objectives: AWS + Kubernetes ingestion, product/team/customer allocation, and quantified ROI, then imported a year of historical billing data to prove trends, not snapshots.
- Modeled shared-service allocation using proportional usage-based splits, and answered the methodology questions head-on: reservation coverage, anomaly detection, and how shared platform costs flow to products fairly.
- Recorded short async video walkthroughs (cost-splitting explained with an airplane-seat analogy) so executives who missed live sessions could still follow the business case.
- Survived a champion departure mid-evaluation by having already built relationships across 20+ stakeholders, from engineers to the CFO.
04
Nine Stakeholders Aligned in Three Weeks Healthcare Data Platform
Proof-of-concept to close in 3 weeks across finance, engineering, data governance, and security. Built client-level cost attribution from the customer's real crosswalk files, mapping Databricks workloads to the insurance payers they serve, and the CISO kept routing technical questions back after close. The account expanded 17% at renewal.
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- Connected AWS, Databricks, and Snowflake billing in the first session, then built "data domain" dimensions from the customer's crosswalk files so every Databricks job ID rolled up to the end-client it serves.
- Cleaned up account-naming inconsistencies blocking allocation, and ran a structured financial-dashboard review with the finance team against their real reporting needs.
- Scoped API/IaC automation so onboarding each future client wouldn't require touching the cost model by hand.
- After close, the CISO brought third-party pricing changes to Griffin for impact analysis: pre-sales trust converting into an advisory relationship.
05
Multi-Currency Unit Economics Across Two Clouds Workforce SaaS
Replaced a 3–4 hour, crash-prone Excel allocation process spanning AWS, GCP, and Kubernetes on four continents. The novel problem was currency conversion, solved with an exchange-rate telemetry stream no prior tool in their stack had managed, alongside usage-based splits of shared Cassandra and Kubernetes costs.
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- Designed the CAD/USD conversion as monthly FX-rate telemetry uploads, so finance reporting landed in the currency the business actually budgets in.
- Split shared Cassandra costs by what drives them: storage allocated by event volume, compute by read/write activity, and GKE workloads by app labels with shared cluster costs distributed on CPU/memory.
- Brought GCP discount mechanics (committed-use and sustained-use) and BigQuery reservations into the allocation model rather than leaving them as unallocated noise.
- Configured RBAC and scoped views so sensitive cost data stayed hidden from the wrong audiences (the failure that had sunk their previous Kubernetes cost tool), plus budget alerts delivered into Slack.
06
Per-Customer Costs at Database-Table Granularity Web3 Infrastructure
A non-standard ask other tools deflected: attribute database costs to individual customers at table-level granularity. Designed a telemetry-ingestion workflow for it, connected the full environment live in a single kickoff call, and stayed in the problem until it worked, which the customer's BizOps lead cited as the reason they moved forward.
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- Diagnosed in the first demo that per-customer RDS attribution (~$130K/month of database spend) was the make-or-break requirement, and outlined a custom telemetry path before the POV started.
- Connected AWS plus dev and prod EKS clusters live during the kickoff call, with compute allocation keyed off pod labels.
- Validated the table-to-customer telemetry workflow in a working session against their real data, including a clean CSV contract so their team could produce the feed reliably.
- Designed the maintenance path as API-driven, so new customer onboardings flow into the cost model automatically instead of accruing manual work.
07
Largest Commercial Deal in CloudHealth History VMware / Broadcom
Led the technical sale end to end: automated data ingestions, a complex cost allocation model, and ~20% in actionable cost savings identified for the customer.
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- Automated multi-source billing ingestion where the platform's standard connectors didn't cover the customer's estate.
- Built a cost allocation model complex enough to be the deal's core technical risk, and de-risked it during the evaluation rather than after signature.
- Part of a run of 100%+ quota across 7 consecutive half-years, with 150%+ in 3 of them.
01
Scaled the Commercial SE Function CloudZero
First and only commercial SE. Built the pre-sales motion for the commercial segment from scratch, supporting $2.5M+ ARR across 50+ new logos.
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- Built the reusable assets the motion runs on: demo environments and dashboard templates for cloud unit economics, cost allocation, and Kubernetes spend.
- Wrote Python data-ingestion pipelines (AWS Lambda, S3, REST APIs) to automate cost allocation and margin reporting for evaluations.
- Owns security and compliance as first-line technical contact: RFPs, security questionnaires, and buyer questions on access controls and data handling.
- Coaches new SEs and AEs on discovery, POV execution, objection handling, and demo strategy.
02
Redesigned the Proof-of-Value Process CloudZero
Cut evaluations from 30-day POCs to 2-week technical validations with explicit success criteria scoped up front, for faster time-to-value for customers and a repeatable playbook for the team.
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- Success criteria are written and agreed with engineering and finance stakeholders before any data is connected, so "done" is defined before the clock starts.
- Validations run against the customer's real billing data, structured as working sessions that build the actual allocation model rather than a throwaway demo.
- Ends in a readout that maps each criterion to evidence, which is what compressed 30 days to 2 weeks without cutting rigor.
03
Implemented AI Product SE Helper CloudZero
An AI-powered SE assistant for live customer calls. It pulls from Confluence and a weekly private repo summary to give instant, source-cited answers about CloudZero's rapidly evolving AI Telemetry product. Leads with what's customer-ready, flags what's in-progress, and never guesses.
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- Three layers: an unattended Python sync pipeline (2x-daily for fast-moving docs, weekly for stable repos, scheduled via launchd), weekly AI summarization that distills a 104K-line Swift/Go codebase into a focused reference, and live Jira/Confluence lookups at question time for anything volatile.
- A tiered authority system decides what wins when sources conflict: customer-facing docs always beat code, because "exists in the repo" doesn't mean "supported for customers."
- Every answer cites its source; an uncitable answer becomes "I don't know" with an escalation path, and a stale codebase summary (>7 days) flags itself.
- Response format is tuned for mid-call use: the customer-ready answer first, technical detail second, caveats flagged last.
04
Built CostFormation Brain CloudZero
An AI knowledge corpus that turns any coding agent into an expert at writing CloudZero CostFormation YAML, the code the allocation engine relies on to decide how spend gets allocated. Provided to customers to drop into a project alongside Claude Code, Cursor, Copilot, or Codex for correct, performant definitions.
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- A generate → validate → repair loop: agents must run a ~1,000-line Python validator (16 deterministic lint rules) and fix errors before presenting YAML. The architecture assumes the model will sometimes be wrong and catches it.
- A routing table sends agents to exactly the corpus files a task needs, and a mandatory pre-generation checklist makes compliance observable to the human reviewer.
- 20 worked example patterns (anonymized from real customer configs) with selection metadata, plus an 18-case eval harness including negative tests that verify broken YAML gets caught.
- One corpus shipped in four agent-native formats (CLAUDE.md, .cursorrules, Copilot instructions, AGENTS.md), kept isomorphic so behavior is identical across tools. Public at github.com/griffinsisk/costformation-brain.
05
Built AI Signal Digest CloudZero
Weekly GTM enablement pipeline that pulls signal from competitor changelogs, AI provider releases, customer calls, and internal Slack, then synthesizes a digest that helps reps proactively steer conversations toward AI cost allocation.
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- Deterministic pipeline, probabilistic AI: Python fetches and normalizes ~18 sources; Claude decides relevance and labeling through version-controlled prompts ground-truthed to a written signal-vs-noise spec.
- Every item carries a talk-track safety label (🟢 safe to reference with customers, 🟡 internal awareness only) with conservative tier-based defaults ("when in doubt, label 🟡").
- A field pattern needs 3+ customer calls across 2+ accounts before the digest calls it a trend, and only customer-volunteered signal counts.
- Cached extractions make prompt iteration a 30-second re-synthesis instead of a 25-minute re-run; the whole thing costs about $1.20/week to operate.
06
Created Cross-Product Enablement Program VMware / Broadcom
Pulled together SEs and AEs across the product suite to enable the full team on each other's products, shifting the go-to-market from product and feature selling toward cohesive solution selling.
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- Structured as recurring cross-training where each product's SEs taught the others their demo, discovery questions, and qualifying signals.
- The payoff: any SE could spot and tee up opportunities for the whole portfolio, instead of selling one product into accounts that needed three.
07
Developed Automated Customer Outreach Program CloudHealth
Built an automated outreach system to surface expansion and upsell opportunities from existing accounts, turning cost data into revenue signals while increasing customer adoption and retention.
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- Used customers' own cost data as the trigger: spend patterns and feature-adoption gaps generated outreach with a concrete reason to talk, not a check-in.
- Paired with a scaled NPS program across a 100+ account book, together driving renewal above 90%.
Projects
A few things I've designed and shipped. I care as much about the context design, evaluation, and reliability behind these as the prompts themselves.
CostFormation Brain
Claude · MCP · Multi-agent
An AI knowledge corpus that turns any coding agent into an expert at writing CloudZero CostFormation YAML, the allocation logic that decides how cloud spend gets attributed. Built to work across Claude Code, Cursor, Copilot, and Codex.
- Designed a context architecture that makes agents read a strict ruleset and route through a 9-file corpus before generating, cutting the wrong-but-plausible syntax general models produce by default.
- Built auto-populating org context from a customer's live config plus the CloudZero MCP, with a compact index so context persists efficiently across sessions.
- Packaged one corpus to deploy identically across five AI coding tools by mapping each to its native instruction format.
SE AI Telemetry Helper
Claude Team Projects · Python · launchd
An automated knowledge pipeline that keeps a Claude Team Project accurate enough to use on live customer calls, spanning 9 code repos, Confluence, and a 104K-line Swift codebase that ships weekly.
- Designed a tiered authority system and uncertainty protocol so the assistant never presents an unverified feature as customer-ready, engineering directly against the confidently-wrong failure mode.
- Built an unattended sync pipeline with launchd, a Google Drive bridge, and Python that keeps knowledge fresh on a schedule instead of relying on manual curation.
- Used Claude to distill a 104K-line Swift and Go codebase into a focused technical reference, working around context limits with graceful degradation.
- Tuned a customer-answer-first response format for real-time use mid-call.
AI Signal Digest
Claude CLI · Python · Prompt evals
A weekly GTM enablement pipeline that synthesizes AI and competitive market signal into a digest reps can act on.
- Built multi-source ingestion across multiple external news feeds, customer call transcripts, and internal Slack, with a defined spec for signal versus noise.
- Version-controlled prompts and saved fixtures for regression testing, so output quality doesn't drift as the pipeline grows.
- Ran the full pipeline through the Claude CLI with no API key required.
Contact Sheet
Next.js · TypeScript · Claude vision
A Next.js app that uses Claude to cull and score large photo sets with intent-aware scoring, built from my own photography work.
- Built a production-style web app in Next.js and TypeScript with Claude doing vision-based image scoring against user intent.
- Created an eval-fixtures harness and Playwright tests to measure scoring quality and catch regressions.
- Designed scoring logic that ranks images by what the photographer is selecting for, not generic aesthetics.
Ask AI Agent (This Site)
Claude · Vercel serverless · Prompt caching
The ✦ Ask AI tab on this site: a corpus-grounded assistant that cites its sources, refuses to improvise, and explains its own architecture if you ask. One serverless function, a prompt-cached knowledge corpus, and a smoke-test suite probing its grounding behavior.
- Deliberately no RAG: the whole corpus ships as a cached prompt prefix, because at this scale retrieval would only add latency, cost, and the one failure mode the agent is designed against: answering without seeing the relevant section.
- A behavior contract enforces citation, an honest "Griffin hasn't shared that," and polite refusal of off-topic use, probed by a smoke-test script after every corpus change.
- Layered abuse guards: input and history caps, per-IP rate limiting, capped output, disconnect-abort to stop paying for abandoned streams, and a provider-side spend limit as the backstop.
- Ask it "why don't you use RAG?" and it will explain this decision itself, with the section citation.
Contact
Email
griffinjsisk@gmail.com
Location
Boston, MA & New York City, NY
LinkedIn
griffin-sisk-68a0b6ab ↗
GitHub
griffinsisk ↗
Instagram
griffsisk ↗
Medium
@griffinjsisk ↗
Ask AI
An assistant grounded in a curated corpus about my work. It cites where each answer comes from, and says so plainly when something isn't covered. The assistant is itself one of my projects: ask it how it works.
Answers come from a fixed corpus, not live data. Conversations aren't stored.