MLOps Engineer
Core
Own tooling and infrastructure to enable ML engineers to efficiently iterate on models, prompts, and datasets, and deploy AI systems into production.
Role type
MLOps Engineer
Builds
Reproducible dataset pipelines, automated experiment workflows, and Terraform-based cloud deployments for ML models and agents.
Domain
Artificial Intelligence / Machine Learning Operations
Deliverable
production ML models
Required skills
Terraform, Python, Docker, Kubernetes, MLflow, Apache Airflow, CI/CD, data versioning, feature stores, cloud providers (AWS/GCP/Azure)
Preferred skills
GPU orchestration, IaC security scanning, policy-as-code, real-time streaming, open-source contributions
Technologies
Terraform, Delta Lake, Apache Iceberg, Great Expectations, MLflow, torchx, Apache Airflow, Prometheus, Grafana, Datadog, NVIDIA DGX, Karpenter, Ray, Kafka, Flink
Responsibilities
Design version-controlled data pipelines and implement data validation systems; Build and maintain experiment orchestration and automation for hyper-parameter sweeps; Author and maintain Terraform modules for ML infrastructure; Implement CI/CD workflows for packaging and deploying models/agents; Integrate observability tools and set SLIs/SLOs for data and model performance.
Seniority
Mid-Senior, hands-on IC
