About The Role
The MLOps Engineer builds and operates the infrastructure that moves machine learning models from experimentation into reliable production services. The role spans training pipelines, model registry, deployment automation, observability, and the cloud systems required to serve models at scale.
Working with ML engineers, data scientists, and platform engineers, this role improves the speed and safety of model releases while maintaining uptime, latency, reproducibility, and data quality. The position is remote, with a preference for candidates based in Austin, TX.
Key Responsibilities
- Build and maintain repeatable training, validation, and deployment pipelines using Python, Docker, Kubernetes, and CI/CD tooling
- Automate model packaging, versioning, promotion, and rollback across development, staging, and production environments using MLflow, Kubeflow, or equivalent platforms
- Deploy and operate batch and real-time inference services on AWS, GCP, or Azure, optimizing infrastructure for reliability, cost, and latency
- Implement monitoring for model performance, data drift, feature quality, service health, and infrastructure capacity using tools such as Prometheus, Grafana, and cloud-native observability platforms
- Manage scalable feature and data workflows with technologies such as Airflow, Spark, Kafka, or managed cloud equivalents
- Define infrastructure as code and secure deployment practices using Terraform, Helm, Kubernetes, IAM, secrets management, and automated testing
- Partner with research and engineering teams to establish reproducibility standards, incident response procedures, and production readiness reviews
What We Are Looking For
- 3–8 years of experience in MLOps, machine learning engineering, platform engineering, or a closely related discipline, including hands-on ownership of production ML systems
- Strong Python and Linux skills, with practical experience building APIs, automation, testing, and operational tooling
- Experience deploying and operating machine learning workloads on AWS, GCP, or Azure using Docker and Kubernetes
- Proficiency with CI/CD, infrastructure as code, and version control using tools such as GitHub Actions, GitLab CI, Jenkins, Terraform, or equivalent technologies
- Working knowledge of model lifecycle management, experiment tracking, feature pipelines, model serving, and monitoring for drift and performance degradation
- Bachelor’s degree in computer science, engineering, mathematics, or a related technical field, or equivalent practical experience
- Bonus: experience with GPU scheduling, Ray, Spark, Kafka, MLflow, Kubeflow, Feast, Datadog, Prometheus, Grafana, or large-scale LLM inference systems