ML Ops Infrastructure Engineer
Build and maintain ML infrastructure pipelines to deploy research models to production at scale, including CI/CD, A/B testing, monitoring, and optimization for low-latency voice AI serving. Requires 4+ years MLOps experience with Python, Docker, Kubernetes.
About the job
What You'll Do
- 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
Requirements
- 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
Nice-to-Haves
- 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
Skills
Python, CI/CD, Docker, Kubernetes, Ml Pipelines, Model Deployment, A/B Testing, Monitoring, Prometheus, Grafana, Terraform, Nvidia Triton, TensorRT, Onnx Runtime, Gpu Optimization
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