GTM AI Operations Specialist

Hewlett Packard Enterprise•All, California, United States of America, CA

2d•$71,500 - $164,400•Hybrid

About The Position

This role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office. Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE. We are looking for a GTM AI Operations Specialist to design, build, and deliver AI agents that sellers use every day. This is a hands-on role focused on writing prompts, configuring agents, testing outputs, and launching seller-facing tools that reduce friction and help close revenue faster. This role connects Sales Operations, AI tooling, and enterprise data to put working AI agents directly in front of sellers. You will turn GTM priorities into seller-facing agents and automated workflows using platforms like Salesforce Agentforce, Microsoft Copilot Studio, Claude Code, Gemini, Snowflake, and Databricks.

Requirements

3 to 5 years in Sales Operations, RevOps, GTM strategy, or AI operations, with hands-on experience putting prompt-engineered workflows or LLM agents into production.
Proven ability to write complex prompts and build agentic workflows on enterprise AI platforms such as Salesforce Agentforce, Microsoft Copilot Studio, Claude Code or Claude for Work, Gemini Enterprise, or ChatGPT Enterprise.
Strong working knowledge of Salesforce and comfort with enterprise data environments like Snowflake and Databricks.
A builder's mindset. You enjoy testing, troubleshooting, and shipping working automations rather than just putting together slide decks.
Comfortable using data through SQL, Power BI, or spreadsheets to track adoption, agent performance, and productivity gains in the field.

Nice To Haves

Experience with revenue intelligence tools like Clari, Gong, People.ai, and other GTM tools.
Relevant certifications are a plus, including Salesforce Agentforce, Microsoft Copilot accreditation, Azure AI Fundamentals, or recognized prompt engineering or enterprise AI certifications.

Responsibilities

Design and deploy autonomous agents: Prototype, test, and ship AI agents and custom copilots that handle key seller motions, including deal inspection, account research, opportunity updates, and meeting prep.
Optimize prompts: Write, refine, and maintain prompt templates, system instructions, and context that give sales teams accurate, consistent, and relevant output.
Tune agent behavior: Decide how agents interact with sellers and prospects. Set the guardrails, tone, persona, and fallback logic across platforms like Salesforce Agentforce and Microsoft Copilot Studio.
Ship production workflows: Re-engineer revenue processes by connecting LLMs and agentic workflows to our core GTM systems such as Salesforce, Clari, Zoominfo, People.ai, Gong.
Prototype and iterate quickly: Build fast, test in the field, and refine using tools like Microsoft CoPilot, Claude Code, ChatGPT Enterprise, Gemini, and orchestrators such as Power Automate, or direct API integrations.
Build for scale: Document agent logic, prompt structures, and standard procedures so the automations you build can grow across the global GTM organization without depending on one person.
Test prompts and models: Run structured experiments on system prompts, context retrieval, and model selection to improve output quality, seller adoption, and deal velocity.
Monitor live performance: Track, audit, and improve agent behavior and prompt accuracy in production, and fix output drift or performance gaps as they show up.
Use enterprise data: Work with the Data and BI teams to tap into Snowflake and Databricks so agents can draw on real-time customer and pipeline data.
Drive field adoption: Build practical prompt libraries, quick-reference playbooks, and short training sessions that show sellers how to get real value from these tools.
Keep it responsible: Make sure every prompt, workflow, and data pipeline meets our security, privacy, and responsible AI standards.