ML Infrastructure Engineer

This is a hands-on infrastructure engineering role at an early-stage enterprise AI company building a context and data governance layer for AI agents in highly regulated industries. You will own the inference and model-serving infrastructure end to end, ensuring AI agents run reliably, accurately, and at scale in production environments where performance is non-negotiable.

Design, build, and operate inference and model-serving infrastructure from development through production deployment.

Scale systems to support AI agents running reliably under increasing concurrency and production load.

Identify and resolve infrastructure bottlenecks in close collaboration with ML and platform engineering teams.

Optimize systems for latency, throughput, and reliability at scale.

5 or more years building and operating machine learning inference systems, model-serving platforms, or ML infrastructure in production environments.

Hands-on experience designing and scaling inference serving infrastructure using tools such as TensorFlow Serving, TorchServe, Triton, KServe, or equivalent custom systems.

Strong systems engineering fundamentals with expertise in distributed systems, containerization, and orchestration (Docker, Kubernetes).