Senior ML Ops Engineer
Core
Design and implement scalable machine learning infrastructure and production lifecycles to move models from experiment to production.
Role type
Senior MLOps Engineer
Builds
CI/CD pipelines, model orchestration, automated training pipelines, feature stores, model registries, and Kubernetes-based development clusters.
Domain
Travel search metasearch (flights, stays, rentals) / Machine Learning Infrastructure
Deliverable
production ML models
Required skills
ML platform operations, containerization (Docker), orchestration (Kubernetes), Linux internals, model serving at scale, ML lifecycle tooling, SLO definition, observability (Prometheus, Grafana, Datadog), Python, infrastructure modernization.
Preferred skills
None stated.
Technologies
Kubernetes, Docker, Prometheus, Grafana, Datadog, Python.
Responsibilities
Build and maintain ML infrastructure end-to-end including CI/CD and automated training pipelines; Own model deployment and serving standards; Develop core MLOps capabilities like feature stores and model registries; Operationalize infrastructure for the ML team including Kubernetes autoscaling and GPU provisioning; Improve platform reliability and performance via advanced observability; Empower Data Scientists through standardized, optimized workflows.
Seniority
Senior, hands-on IC