AI Product Engineer

Tennr•New York City, NY

16d•Hybrid

About The Position

We’re looking for a high-agency AI Product Engineer to bridge the gap between cutting-edge ML research and real-world product delivery . You’ll design and build agentic workflows that power complex healthcare processes, combining LLMs, vision models, and structured automation to solve hairy infrastructure, workflow, and UX challenges in U.S. healthcare. This role involves creating pipelines , evaluation harnesses, and scalable production-grade agents . You’ll be responsible for researching the right tools and models , ensuring we always use the best-fit technology for each workflow. As part of our ML team , you’ll work hand-in-hand with researchers to translate new techniques and insights into production-ready systems. The ideal candidate has full-stack engineering experience , hands-on expertise with LLM APIs , and a proven track record of deploying AI systems into production . You’ll work across the stack—from data collection to orchestration to frontend integration —bringing cutting-edge AI solutions to life in real-world healthcare environments.

Requirements

Proficiency in full-stack development, with a strong understanding of web frameworks, backend systems, and cloud infrastructure
A track record of working through the full lifecycle of building, testing, deploying, scaling, and monitoring LLM-centered software architectures.
Understanding of how to test AI system performance beyond raw model accuracy.
Must articulate ideas well! A big part of making successful AI systems is telling people about them. This includes writing docs and technical reports at the minimum – and speaking in front of an audience at the extreme (optional).

Nice To Haves

Experience in healthcare or working with unstructured documents is a plus.

Responsibilities

Ship full-stack AI systems end-to-end
Build observability and debugging tools to capture model performance, user feedback, and long tail cases in production.
Go from ideation to code within hours; iterate quickly on experiments and data.
Work directly with ML, Sales, Customer Success, and Implementations to build AI systems for different use cases