As a GTM AI Engineer on the AI Operations team, you'll be at the forefront of our AI strategy, getting hands-on with cutting edge technology to solve real-world business problems. You will be an internal AI development expert, building (and subsequently owning) a suite of agents, automations, and full stack applications that our 2500+ member GTM team will leverage on a daily basis, with coverage from our SMB to Strategic segments. You will own the full development lifecycle for internal AI tools, encompassing everything from initial prototyping and proof-of-concept development to scaling solutions for production, conducting beta tests with end-users, and providing ongoing maintenance. Your responsibilities will include constructing sophisticated agentic tools using platforms like Glean and N8N, developing full stack applications using Cursor and Google Cloud. The solutions you make will have a measurable impact on our organization – with each solution you launch having a user adoption and financial impact target that you own.

We are seeking an individual with an experimentation mindset, who is not afraid to try new ideas, learn from failures, and is energized by the prospect of rapid prototyping and testing. The ideal candidate is deeply passionate about AI, believing in its potential to fundamentally reshape the future of work and eager to be a part of this transformation. You should possess a strong understanding of AI fundamentals, such as context windows, token consumption, compute costs, chunking, and performance benchmarks, along with knowledge of API usage, scripting, SQL/SOQL, and Python fundamentals. Experience with prompt engineering for complex tasks, leveraging foundational models (like Gemini, GPT, Claude, etc) through both web interfaces and APIs, and familiarity with the evolving agentic landscape, including orchestration layers and MCP, are also essential.

What You'll Do:

• Own the full stack development cycle of prototyping / developing proof of concepts, scaling prototypes for production use, beta testing with end users, and performing ongoing maintenance for internal AI tools
• This includes building complex agents using Glean and N8N, as well as using AI enhanced IDE tools such as Cursor to develop a range of agents, workflows, and full stack applications
• Internally developed tools may be tested head-to-head with external vendors to determine the efficacy of the solutions we build
• Collaborate with our internal product managers and Enterprise Systems teams to integrate key data sources into your applications / agents in a secure way that respects and adds to Motive's data governance structure
• Ensure agents and applications follow UX best practices, meet users where they already work, and can be easily leveraged by a less technical business audience
• Have a strong POV on when an agentic workflow is warranted vs a business logic focused solution
• Have a strong POV on which of our internal development tools is best suited to solve a specific use case
• Manage a provided cost envelope for production use cases, testing different LLM models to balance performance with token consumption cost
• Create and maintain documentation around launched solutions
• Train end-users on the usage of launched tools
• Stay up-to-date on the latest advancements in LLM's and agentic orchestration

What We're Looking For:

• Own the full stack development cycle of prototyping / developing proof of concepts, scaling prototypes for production use, beta testing with end users, and performing ongoing maintenance for internal AI tools
• This includes building complex agents using Glean and N8N, as well as using AI enhanced IDE tools such as Cursor to develop a range of agents, workflows, and full stack applications
• Internally developed tools may be tested head-to-head with external vendors to determine the efficacy of the solutions we build
• Collaborate with our internal product managers and Enterprise Systems teams to integrate key data sources into your applications / agents in a secure way that respects and adds to Motive's data governance structure
• Ensure agents and applications follow UX best practices, meet users where they already work, and can be easily leveraged by a less technical business audience
• Have a strong POV on when an agentic workflow is warranted vs a business logic focused solution
• Have a strong POV on which of our internal development tools is best suited to solve a specific use case
• Manage a provided cost envelope for production use cases, testing different LLM models to balance performance with token consumption cost
• Create and maintain documentation around launched solutions
• Train end-users on the usage of launched tools
• Stay up-to-date on the latest advancements in LLM's and agentic orchestration