ML Ops Infrastructure Engineer

Stack

Responsibilities

• ▹Design and build CI/CD pipelines tailored for ML model development, validation, and deployment
• ▹Architect and maintain model deployment pipelines from research to staging to production
• ▹Build A/B testing infrastructure for controlled rollouts and real-world performance measurement
• ▹Implement monitoring for model performance: accuracy metrics, latency, drift detection, regression alerts
• ▹Develop automated retraining pipelines triggered by data changes or performance degradation
• ▹Create build/test environments that mirror production for high-fidelity researcher feedback
• ▹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

Requirements

• ▹4+ years of experience in MLOps, DevOps, or infrastructure engineering focused on ML systems
• ▹Strong proficiency in Python, building automation and tooling for ML workflows
• ▹Deep experience with CI/CD systems and 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
• ▹Familiarity with model evaluation, validation, and QA processes
• ▹Understanding of monitoring and observability principles for ML systems
• ▹Strong problem-solving skills with a bias toward automation

Nice to have

• ▹Experience with model serving frameworks such as NVIDIA Triton, TensorRT, or ONNX Runtime
• ▹Background in speech, audio, or real-time media ML systems
• ▹Experience with IaC tools such as Terraform or Pulumi
• ▹Hands-on experience with monitoring/observability stacks (Prometheus, Grafana, Datadog)
• ▹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

Soft skills

About the company

Deepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text (STT) and text-to-speech (TTS), and powering production-grade voice agents at scale. More than 200,000 developers and 1,300+ organizations build voice offerings on Deepgram's technology, including Twilio, Cloudflare, and Sierra. Backed by a recent Series C, Deepgram has processed over 50,000 years of audio and transcribed more than a trillion words.