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ML Ops Infrastructure Engineer

Deepgram· Engineering
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Remote$160k - $220k + equityFull-timePosted 6mo ago

About the role

ML Ops Infrastructure Engineer at Deepgram responsible for building pipelines, deployment systems, and testing infrastructure to take ML models from research to production at scale, enabling safe, fast, and reliable delivery of models to real-time voice AI APIs.

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

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

Skills

Required:PythonCI/CDDockerKubernetesModel deployment/servingMonitoring/Observability for ML
Nice to have:Canary / progressive delivery (ML)Model quality gates
Last updated Sep 30, 2026. Interested? Apply on the company site.
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About Deepgram

Building foundational AI for speech transcription and understanding.

Deepgram is a foundational AI company on a mission to understand human language. We give any developer access to the most advanced speech AI transcription and understanding with just an API call. Our models deliver the fastest, most accurate transcription alongside contextual features like summarization, sentiment analysis, and topic detection. Beyond that, developers can: 🔊 Process live-streaming or pre-recorded audio 🌎 Transcribe in dozens of languages ⚙️ Train custom models for unique use cases 🔑 Access deep NLU with a unified API 💻 Build in any programming language with our SDKs ✅ Deploy on-prem or on DG’s managed cloud 📈 Get scalable GPU infra for training and inference Deepgram is a proud NVIDIA partner and Y Combinator company, and we recently completed a $72M Series B to define the future of AI Speech Understanding, making us the most-funded speech AI company at its stage.

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