MLOps / Infrastructure Engineer

Tech Stack

Responsibilities

• Design and maintain cloud infrastructure (GCP or AWS) to support real-time model serving, data ingestion, and evaluation workflows.
• Deploy and optimize APIs for low-latency access to ML models and embedding search systems.
• Manage and optimize the end-to-end training data flow—from sourcing and cleaning datasets to preparing them for model consumption.
• Build observability tooling for production ML pipelines (monitor latency, error rates, request volumes, drift).
• Automate model deployment, retraining, and evaluation pipelines (CI/CD for ML).

Benefits

• 401k
• Health Insurance
• Learning Budget
• Remote Work

Culture

Requirements