Senior Product Manager, Internal AI & Automation

Apptegy

1d•$140,000 - $210,000•Remote

About The Position

As Senior Product Manager, Internal AI & Automation, you will own the product layer of Apptegy's internal AI function. You will work directly with leaders and teams across Sales, Customer Success, Marketing, Finance, People, and other parts of the business to identify high-value workflows where AI can materially improve how work gets done. You will turn those opportunities into a focused roadmap, build and test the first working versions directly, and partner with our AI Ops Engineer to make the solutions reliable and scalable. You will own the product decisions, success metrics, rollout plans, and adoption of the tools you launch. This is a senior individual contributor role on a lean, high-visibility team. You will be expected to move from ambiguity to action without waiting for a fully defined roadmap, make clear tradeoffs about where to focus, and stay close to the details of the workflows and tools you are changing. You will partner with the VP of Product and Chief Product and Technology Officer to translate company priorities into a practical portfolio of internal AI products, then help shape how this function grows over time.

Requirements

5-10 years of product management experience. Your résumé is full of things you built, not just things you planned.
You are a systems thinker. You understand the people an agent serves and how their work actually flows: where decisions happen, where context gets lost, and where things slow down. You assume the requests and complaints you receive are incomplete, and you look for the need that is actually worth solving.
You are hands on with agents. You do not just write requirements and hand them off. You work directly with prompts, documents, tools, and delegation, prototype against real runs, and partner with engineering on harder capabilities.
You have strong intuition for LLMs: how they are prompted, how they delegate to one another, and how they are evaluated. You know what makes an agent accurate, reliable, on tone, and trusted, and you stay current on what is newly possible.
You thrive in ambiguity. When the problem is half defined and the path is not clear, you start, learn, and adjust without waiting for perfect conditions.
You have built AI tools, automation workflows, or internal tools before. You know what zero to working looks like and have done it without waiting for a fully staffed engineering team.
You are technically fluent enough to build V1s independently, comfortable with LLM APIs, agent frameworks, and workflow automation tools, and collaborative enough to hand off cleanly when it is time to systematize.
You have strong cross functional instincts. You build credibility quickly with different kinds of stakeholders, get into the details of how teams work, and translate what you find into something buildable.
You have a track record of driving measurable outcomes, not just shipping features.

Nice To Haves

You've worked closely with business teams and understand how operational workflows actually run day-to-day — not just what's in the process doc, but what's really happening.
Experience deploying AI agents in an operational context — tools like Gong, Intercom, Salesforce, or Glean have been part of your work.
Comfort working with the modern AI and automation stack: Claude or OpenAI APIs, n8n for workflow orchestration, Glean or Snowflake for data access, and the ability to stitch them together into something a real team will actually use.
You've redesigned a human workflow around AI — not bolted AI onto an existing process, but genuinely rethought how the work gets done.
Familiarity with B2B SaaS operations and the levers that drive efficiency at scale.

Responsibilities

Run rapid discovery across operational teams to identify high-value, repeatable workflows where AI can drive step-change improvements — not just incremental automation, but fundamentally different ways of working.
Own V1 end-to-end: use LLM APIs, no-code, and low-code tools to get to a working prototype fast, validate it with real users, and prove out the value before investing in scale.
Partner with your AI Ops Engineer to systematize what works — turning scrappy V1s into production-grade tools that are reliable, maintainable, and built to last.
Treat internal teams as your clients — understand how they actually work, find the real friction, and build tools that earn a permanent place in their daily workflow.
Redesign workflows from first principles — if AI handles the repetitive work, what does this role actually look like?
Ship rollout plans and usage playbooks alongside every tool you launch, so adoption isn't left to chance.
Define success metrics for every tool you ship and own adoption within the teams using them.
Maintain a visible backlog of automation opportunities, sequenced by business impact, and communicate priorities clearly to leadership.
Stay current on the rapidly evolving AI and automation landscape and surface relevant tools, models, and patterns that could accelerate the work.