Briefing Document: Leon Basin's GTM Engineering Campaign
A comprehensive briefing on strategy, assets, pipeline, and execution
Executive Summary
This briefing synthesizes a multi-faceted career campaign undertaken by Leon Basin, centered on securing a high-income role by establishing a unique professional brand as a "GTM Engineer." The strategy is a deliberate pivot from a traditional 15-year enterprise sales career to a hybrid "Builder-Seller" persona, combining senior Go-To-Market strategy with hands-on Python coding and AI agent development.
The primary objective is to generate significant annual income through a dual-pronged approach: prioritizing a full-time GTM Engineering role while simultaneously operating a fractional consulting business, "Basin & Associates," to provide bridge income. The campaign is characterized by meticulous planning, disciplined execution, and the creation of tangible technical assets used as strategic props in interviews and applications.
1. Core Strategy: The "GTM Engineer" Persona
The campaign's foundation is the repositioning of Leon Basin from a "Director of Sales" to a "GTM Engineer"—a sales leader who architects and builds the automated systems required for modern revenue generation.
Core Premise
The bottleneck in sales is "human latency." The solution is to replace manual processes with "Agentic Workflows."
Income Objective
A target of significant annual income for the year. A realistic path is identified as a Full-Time Equivalent (FTE) role generating substantial income for the remainder of the year, supplemented by additional income from 2-3 consulting retainers.
2. Key Assets & Technical Proof Points
The GTM Engineer brand is substantiated by a collection of functional digital assets and quantifiable achievements. These are consistently leveraged as proof of work.
| Asset / Metric | Description |
|---|---|
| basinleon.github.io | The primary portfolio site with clear positioning: "I Don't Just Sell Tech—I Build AI Agents." It features case studies and an ROI calculator. |
| enrich_company.py Script | A Python script that functions as a "GTM Intelligence Engine," simulating an agent that enriches a company domain with tech stack and hiring intent data. This is used as a live demo or "magic trick" in interviews. |
| Agentic GTM System | A four-agent swarm (Prospector, Scoring, Personalization, Coordinator) built for a technical assessment. It processes raw data to produce a prioritized prospect list with personalized outreach hooks. |
| BASIN::NEXUS OS | A proprietary "Revenue OS" comprising over 83,000 lines of production-grade code. |
| Quantifiable Results | Consistently cited metrics include 160% pipeline growth, significant yield from a bifurcated GTM model, $424k in annual savings, and replacing significant manual labor costs with automation. |
3. Job Search Campaign: Execution and Pipeline
The job search is executed with strategic precision, treating each company and interview as a distinct tactical engagement.
3.1. Core Tactics
"Code-Switching"
Adopting different personas based on the company profile:
- The Builder / GTM Engineer: A scrappy, technical persona for startups ("Hoodie Energy").
- The Executive / Revenue Architect: A polished, strategic consultant persona for enterprise firms ("Suit Energy").
Live Demos
Using the enrich_company.py script as a "prop" to shift from discussion to a live demonstration of capability, reframing it as "Data Modernization" for enterprise targets and "manufacturing leads" for builder targets.
"Founding Engineer" Framing
Answering technical questions by focusing on business value and scalability (modularity, queuing, LLM integration) rather than simple implementation details.
Network Intelligence
Analyzing engagement signals to gauge interest from target companies and identify peers for brand validation (other GTM Engineers).
3.2. High-Priority Pipeline
| Company | Role / Context | Strategy & Status |
|---|---|---|
| Technology Company | GTM Engineer | Final Stages. A comprehensive technical exercise (Agentic GTM System Architecture) was submitted. Post-interview follow-up strategy focuses on demonstrating continued value. |
| Technology Services Firm | Bay Area Hunter | Interview Scheduled. Strategy is to position as a "Hunter who can build the Data Fabric," bridging their staffing past with their technology future. The hook: "I can walk into a Prime Account, audit their messy data stack, and scope a significant modernization project." |
| Technology Startup | GTM Engineer (Marketing) | Interview Scheduled. Strategy is to align with their advanced product by presenting as a "Builder" who can create an equally advanced GTM engine. The hook: "Your advanced products need a GTM architecture that is just as automated as the product itself." |
| Analytics Company | Growth Sales Leader | Deep in the funnel, with competitive compensation package confirmed. |
| Infrastructure Company | Sr. Dev Relations - Agentic AI | Referral officially submitted. |
3.3. Application Sprint Targets
A focused effort was made to apply to high-signal roles that fit the GTM Engineer persona, using tailored cover notes.
| Company | Role | Tailored Angle |
|---|---|---|
| AI Platform Company | Product Engineer, GTM Innovation | "Building the 'Revenue feedback loop' vs. just selling." |
| Developer Tools Company | Sr. Enterprise AE (Agentic) | "A Seller who actually lives in VS Code." |
| Security Technology Company | GTM Engineer | Recycling the AI security company pitch for "Physical Security + AI." |
| AI Monitoring Company | BD & Partnerships Manager | "You can't have reliable AI partnerships without reliable AI monitoring." |
| Data Infrastructure Company | Strategic Account Director | "AI is only as good as the data feeding it... I can technically qualify a client's data readiness." |
4. Fractional Consulting: Basin & Associates
To generate bridge income, a consulting business is actively managed, reinforcing the "System Architect" brand.
Entity & Status
- Entity: Basin & Associates
- Status: Secured signed revenue with clients in various technology sectors
Onboarding Process
A systematized onboarding email template is used to establish an executive presence. It requests specific "Targeting & Asset data" required to "build the initial outreach architecture."
Strategic Language
The communication deliberately uses phrases like:
- "calibrate the targeting engine"
- "automate budget qualification"
- promises a "Signal Report" as a premium deliverable
This positions the service as a system build, not just contract work.
5. The "Agentic GTM" System Architecture
The technical exercise submitted to a target company provides a blueprint for the GTM Engineer philosophy. It reimagines the BDR function as an "Event-Driven Agent Swarm" that filters for Intent over Activity.
Three-Stage Workflow
1. INGEST (The Eye)
A Prospector Agent unifies data from Firmographics (CSV), Engagement (CSV), and Trigger Events (JSON).
2. REASON (The Brain)
A Scoring Agent applies context logic:
- A "Data Breach" event acts as a +50 Score Multiplier
- A "Competitor Launch" event triggers a Kill Switch (Score = 0)
3. ACTION (The Output)
A Personalization Agent crafts outreach based on the trigger event, leading to a "Prioritized Sales Sequence." Competitor accounts are routed to a "Strategy/Intel" path.
Production Roadmap
The plan includes scaling the MVP by:
- Integrating LLMs for dynamic personalization
- Moving to real-time event streams via webhooks
- Creating a reinforcement learning feedback loop with Salesforce data to auto-tune scoring weights
6. Key Talking Points and Strategic Answers
A core component of the campaign is a well-rehearsed set of "Founding Engineer" answers designed to demonstrate strategic thinking over rote technical knowledge.
| Question / Topic | Strategic Answer / Quote |
|---|---|
| "Why this scoring algorithm?" | "I built it to solve the 'False Positive' problem... I separated Motion (Visits/Clicks) from Context (Events). Context acts as a Multiplier... A 'Competitor Product Launch' is a Kill Switch." |
| "Why this agent-based architecture?" | "I designed for modularity. If we want to change the Data Source... I only update the prospector_agent. This prevents 'spaghetti code' as we scale." |
| "How would you scale this?" | "This Python script is the MVP... To scale... move from CSV read to Webhook Listeners... Decouple the agents using a message queue like Celery or Kafka... Replace if/else logic... with an LLM call." |
| The "Why Are You Leaving Sales?" Answer | "I'm not leaving sales—I automated it. After 15 years, I realized the bottleneck wasn't effort—it was 'human latency.' ... I built the system that closes that gap." |
Campaign Philosophy
The GTM Engineer campaign represents a fundamental shift from traditional sales leadership to technical revenue architecture. It's not about leaving sales—it's about automating the system that makes traditional sales activity irrelevant.
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