ML Ops Infrastructure Engineer

The role

You will own the bridge between research and production by building pipelines, deployment systems, and testing infrastructure. This role involves moving models from experimental stages to production APIs serving millions of requests.

What you will do

• Design and build CI/CD pipelines for ML model development, validation, and deployment
• Architect and maintain model deployment pipelines from research to production
• Build A/B testing infrastructure for controlled rollouts and performance measurement
• Implement monitoring for model performance including accuracy, latency, drift, and regression
• Develop automated retraining pipelines
• Create and maintain build and test environments
• Establish model versioning, artifact management, and rollback capabilities
• Collaborate with research engineers to define and enforce model quality gates
• Build observability dashboards for real-time model health insight
• Optimize model serving infrastructure for latency, throughput, and cost efficiency

Required qualifications

• 4+ years of experience in MLOps, DevOps, or infrastructure engineering with a focus on ML systems
• Strong proficiency in Python
• 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
• 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

Nice to have

• Experience with model serving frameworks such as NVIDIA Triton Inference Server, TensorRT, or ONNX Runtime
• Background in speech, audio, or real-time media ML systems
• Experience with Infrastructure as Code tools such as Terraform or Pulumi
• Hands-on experience with monitoring and observability stacks (Prometheus, Grafana, Datadog, or similar)
• Familiarity with GPU-accelerated inference optimization and profiling
• Experience with feature stores, data versioning, or ML metadata management
• Knowledge of canary deployment strategies and progressive delivery for ML models

About the company

Deepgram is a research company that productizes as fast as possible. Their speech models and real-time infrastructure power AI systems for more than 5,000 organizations.