ML Ops Infrastructure Engineer

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