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 deployed in highly regulated industries. You will own the inference and model-serving infrastructure end to end, making production AI agents fast, reliable, and scalable as concurrency grows.

Design, build, and own inference and model-serving infrastructure from initial architecture through production deployment.

Scale systems that enable AI agents to run reliably and efficiently under increasing concurrent load.

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

Drive performance optimization across latency, throughput, and reliability for production workloads.

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

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

Strong distributed systems fundamentals, including experience managing concurrent requests and resource allocation under load.

Proficiency with containerization and orchestration technologies, particularly Docker and Kubernetes, for ML workloads.

Experience with cloud infrastructure platforms (AWS, GCP, or Azure) for deploying and managing ML systems.

Solid monitoring and observability skills using tools such as Prometheus, Grafana, ELK, or distributed tracing solutions.

Proficiency in at least one systems or backend language: Python, Go, Rust, C++, or Java.

Familiarity with knowledge graphs, semantic search, or graph databases is a plus.

Background in agentic or autonomous AI systems, real-time inference, or enterprise data infrastructure is a plus.