ML Ops Infrastructure Engineer
Core
Build CI/CD pipelines, deployment systems, and testing infrastructure to move ML models from research notebooks to production APIs serving millions of requests.
Role type
Senior IC ML Ops Infrastructure Engineer
Builds
Production-grade ML serving infrastructure, automated retraining pipelines, and observability dashboards for voice AI models.
Domain
Voice AI / Real-time media / Cloud Infrastructure
Deliverable
production ML models
Required skills
Python, CI/CD pipeline design, Docker, Kubernetes, ML model deployment, model evaluation, monitoring/observability, automation
Preferred skills
NVIDIA Triton Inference Server, TensorRT, ONNX Runtime, Terraform, Prometheus, GPU inference optimization, feature stores, canary deployment
Technologies
Python, Docker, Kubernetes, Terraform, Prometheus, Grafana, Datadog, NVIDIA Triton, TensorRT, ONNX Runtime
Responsibilities
Design and build CI/CD pipelines for ML model development and deployment; Architect model deployment pipelines from research to production; Build A/B testing infrastructure for controlled model rollouts; Implement monitoring for model performance, accuracy, latency, and drift; Develop automated retraining pipelines; Create build and test environments mirroring production; Establish model versioning and rollback capabilities; Build observability dashboards for model health; Optimize model serving infrastructure for latency, throughput, and cost.
Seniority
Senior, hands-on IC
