Job Description & Details
This is a heavy-hitting AI Product Owner role where you'll own the roadmap for enterprise voice-agent and conversational AI solutions. If you like dealing with complex multi-agent orchestration, prompt governance, and AWS-based AI deployments rather than just writing superficial user stories, this gig is worth a serious look.
What You'll Actually Be Doing
Your day-to-day will revolve around architecting conversational models that route user intent accurately across various enterprise verticals like sales and servicing. You'll spend a lot of time defining enterprise AI personas, optimizing system prompts, and balancing token efficiency against latency. Expect to wrangle competing priorities from multiple business stakeholders while partnering closely with engineering to build robust automated evaluation pipelines and guardrails.
The Core Tech Stack
You need deep familiarity with Large Language Models, Retrieval-Augmented Generation (RAG), and vector databases. The role demands an advanced grasp of prompt engineering—system instructions, few-shot tuning, and managing context parameters—alongside a strong capability in designing multi-agent workflows or state-machine architectures on cloud platforms like AWS.
Interview Expectations
When you talk to the hiring team, expect them to test your architectural chops by asking how you would handle prompt drift and degradation in a production multi-agent system. They want to see how you balance token cost and latency with conversational accuracy. They're secretly looking for a product leader who doesn't just treat AI like traditional software, but understands the stochastic nature of LLMs and knows how to build robust evaluation datasets and LLM-as-a-judge frameworks to measure real-world performance.
Application Advice
To make your resume clear the ATS and catch the recruiter's eye, make sure your profile explicitly highlights keywords like 'multi-agent orchestration', 'prompt engineering', 'containment rates', and 'LLM evaluation'. Don't just list agile methodologies; provide concrete examples of how you translated messy business operations into precise natural-language interaction flows and scaled generative AI products in enterprise settings.