Production-Scale ML Ops Engineer

Deepgram; San Francisco (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

Deepgram is seeking an ML Ops Infrastructure Engineer to bridge research and production, building pipelines, deployment systems, and testing infrastructure for scalable real-time APIs. You will ensure safe, fast, and reliable delivery of model improvements to customers using Deepgram's voice AI APIs.

You will design automated retraining and monitoring, establish model versioning, and collaborate with researchers to uphold quality gates across environments.

Qualifications

• 4+ years of experience in MLOps, DevOps, or infrastructure engineering with a focus on ML systems.
• Strong proficiency in Python and experience building automation and tooling for ML workflows.
• Deep experience with CI/CD systems and building pipelines for software and model delivery.
• Hands-on experience with Docker and Kubernetes for containerized workload management.
• Practical experience deploying and serving ML models in production environments.
• Familiarity with model evaluation, validation, and quality assurance processes.
• Understanding of monitoring and observability principles as applied to ML systems.
• Strong problem-solving skills and a bias toward automation over manual processes.

Responsibilities

• Design and build CI/CD pipelines specifically tailored for ML model development, validation, and deployment.
• Architect and maintain model deployment pipelines that move models from research environments through staging to production with confidence.
• Build A/B testing infrastructure that enables controlled rollouts of new models and measures real-world performance impact.
• Implement comprehensive monitoring for model performance in production -- accuracy metrics, latency, drift detection, and regression alerts.
• Develop automated retraining pipelines that trigger on data changes, performance degradation, or scheduled cadences.
• Create and maintain build and test environments that mirror production, giving researchers high-fidelity feedback before deployment.
• Establish model versioning, artifact management, and rollback capabilities to ensure safe and reproducible deployments.
• Collaborate with research engineers to define and enforce model quality gates before production promotion.
• Build observability dashboards that give the team real-time insight into model health across all environments.
• Optimize model serving infrastructure for latency, throughput, and cost efficiency

Skills

Python

CI/CD

Docker

Kubernetes

ML systems

Monitoring

Tools

NVIDIA Triton Inference Server

TensorRT

ONNX Runtime

Job description

Deepgram is seeking an ML Ops Infrastructure Engineer to bridge research and production, building pipelines, deployment systems, and testing infrastructure for scalable real-time APIs. You will ensure safe, fast, and reliable delivery of model improvements to customers using Deepgram's voice AI APIs.

You will design automated retraining and monitoring, establish model versioning, and collaborate with researchers to uphold quality gates across environments.