MLOps Engineer

As the first dedicated ML Ops Engineer , you’ll own the tooling and infrastructure that make our ml engineers wildly productive and ensure we are able to efficiently iterate on ML models, prompts, and datasets and deploy our AI systems into a predictable production environment. You’ll bridge the gap between research and DevOps—designing reproducible dataset pipelines, automated experiment workflows, and Terraform-based cloud deployments that scale.

• Design version-controlled data pipelines (feature stores, data registries) using tools such as Delta Lake, Apache Iceberg • Implement systems for data validation, lineage tracking, and automated quality checks (e.g., Great Expectations).

• Build and maintain experiment orchestration with platforms like MLflow, torchx, and Apache Airflow. • Provide templated systems and tools to ML Engineers that easily launch training/evaluation data processing systems • Automate hyper-parameter sweeps and A/B tests, exposing clear dashboards for results.

• workflows that package, test, and promote models and agents through staging to production. • Implement canary deployments and rollbacks for models/agents services