AI GTM Engineer

What You’ll Do

GTM systems architecture and automation

Design and operate multi-agent systems connecting Salesforce, Gong, Slack, Snowflake, and Samba's proprietary TV data assets into unified, real-time GTM workflows

Build and maintain MCP (Model Context Protocol) servers that give Claude governed, real-time access to our GTM stack, including live Salesforce records, Gong call data, and Samba viewership signals

Eliminate manual data transfer and context-switching for sellers by automating the aggregation, summarization, and routing of deal intelligence across tools

Create RAG pipelines and prompt libraries that give Claude accurate, governed context on our products, prospects, pricing, and competitive landscape

Architect integrations between AI services and internal systems using Python and APIs, enriching contact and account records with live signals

Evals, reliability, and continuous improvement

Design and run structured evals across all production AI systems: measure output quality, accuracy, regression risk, and real business impact, not just vibes

Build eval frameworks that catch prompt drift, model behavior changes, and degraded tool use before they hit sellers in production

Run A/B experiments across workflows to prove what is actually moving pipeline and revenue, not just what looks good in demos

Monitor agentic systems in production and own the feedback loop: what broke, why, and what the fix is

Be the internal authority on Claude across the Revenue Org: best practices, MCP architecture, prompt engineering standards, and enablement

Write documentation and run enablement sessions so sellers and operators extract real value from every system you ship

Signal integration across the sales org

Build systems that automatically surface deal risk, flag renewal exposure, draft personalized outreach, and score accounts by conversion likelihood using real viewership signals

Connect Gong call intelligence, Salesforce pipeline data, and Samba TV audience signals into a unified account view that sellers can act on without digging through five tools

Design workflow automation that reduces the cognitive load on sellers so they spend more time selling and less time updating CRM fields, hunting for context, or writing the same email for the 40th time

Identify the highest-friction points in our GTM process and systematically eliminate them through automation

Who You Are

3 to 5 years in software engineering, data engineering, or GTM/revenue operations engineering with serious technical depth

You have owned an AI or automation function, not just contributed to one

Production-level Python and/or JavaScript; you write, ship, and maintain code others depend on

Hands-on LLM production experience: prompt engineering, tool use, multi-turn agentic workflows, RAG

Experience building and operating MCP servers that connect LLMs to live business systems and data sources

A real eval practice: you know how to measure whether an AI system is working, build regression tests, and catch drift before it causes problems

Deep, production-level Claude expertise including the API, structured outputs, tool use, agentic workflows, and system prompt design; you can also teach it and build reliable systems around it

Hands-on experience with Claude Code as a primary development environment

Salesforce hands-on (SOQL, APIs, Flows) plus at least one sales engagement platform, preferably Gong

Snowflake and dbt working knowledge for querying and transforming data to support AI workflows

Genuine understanding of sales pipeline mechanics: forecasting, deal inspection, pipeline hygiene, ABM

Comfortable in both a command line and a boardroom; you move between implementation and executive conversation without losing either audience

Nice to have

Azure (data services, API management) and Power BI

Slack API and workflow automation (Bolt framework or similar)

AdTech, media measurement, or data-driven intelligence background

LLM orchestration frameworks: LangChain, LlamaIndex, LangGraph

MLOps experience: eval infrastructure at scale, drift monitoring, inference cost management

Prior experience at a high-growth data or media company