MLOps Engineer
Core
Build and operate the ML lifecycle platform to enable data science and engineering teams to train, track, deploy, and monitor models reliably in production.
Role type
Senior individual contributor MLOps Engineer
Builds
Reproducible ML lifecycle tooling, CI/CD pipelines, and cloud-native infrastructure for model deployment and monitoring
Domain
Machine Learning Operations, Cloud Infrastructure, Platform Engineering
Deliverable
production ML models
Required skills
ML lifecycle tooling (MLflow, Kubeflow, SageMaker), Kubernetes, Terraform, CI/CD pipeline design, Python, Go, PyTorch, infrastructure security, IAM, secrets management
Preferred skills
GCP, Pulumi, GitHub Actions, Go, data engineering platforms, LLM serving runtimes (vLLM, llama.cpp), ML compiler stacks (LLVM/MLIR), benchmarking frameworks
Technologies
MLflow, Kubernetes, Terraform, Python, Go, PyTorch, GCP, Pulumi, GitHub Actions, vLLM, llama.cpp
Responsibilities
Build and operate the ML lifecycle platform including experiment tracking and model registry; Own CI/CD and deployment for ML workloads including containerization and staged rollouts; Make models observable and reliable in production with monitoring and alerting; Build cloud-native foundations using Kubernetes and infrastructure-as-code; Establish infrastructure-level governance for ML systems; Enable and mentor teams by defining repeatable patterns and providing technical guidance
Seniority
Senior, hands-on IC with mentorship responsibilities
