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About the role
TL;DR
Build and maintain scalable ML infrastructure and automated pipelines for model training, deployment, and monitoring.
- •KAYAK is seeking a Senior MLOps Engineer to focus on the design and implementation of our machine learning infrastructure and production lifecycle.
- •This role bridges the gap between data science and production engineering.
- •Key Responsibilities Build and maintain ML infrastructure end-to-end, including CI/CD pipelines and model orchestration.
- •Own model deployment and serving, ensuring low latency and high availability.
- •Develop core MLOps capabilities like feature stores, model registries, and automated monitoring.
- •Operationalize infrastructure for the ML team, enabling Kubernetes autoscaling and GPU provisioning.
- •Improve platform reliability and performance through advanced observability tooling and automation.
- •Requirements Experience building and operating ML platforms in production environments.
- •Solid working knowledge of containerization and orchestration (Docker, Kubernetes).
- •Familiarity with ML lifecycle tooling, including orchestration frameworks, feature stores, and model registries.
- •Experience owning production systems: defining SLOs, building observability, and participating in incident response.
- •Comfort writing production-quality code in Python or a comparable language.
Required skills
PythonDockerKubernetesCI/CDPrometheusGrafanaDatadogGitMLflowKubeflow
Domain expertise
travel
Benefits & perks
Work from (almost) anywhere for up to 20 days per year, Company-paid therapy sessions through SpringHealth, Company-paid subscription to HeadSpace, Company-wide week off a year, No meeting Fridays, Paid parental leave, Paid volunteer time, Development Dollars, Leadership development, Access to thousands of on-demand e-learnings, Travel Discounts, Employee Resource Groups, 6 weeks paid vacation + a day off for your birthday, Free lunch 2 days per week, Pension plan contributions, Public transportation subsidies, Bike leasing program, Monthly social events, Thursday happy hours, sports teams
Tech stack
PythonDockerKubernetesCI/CDPrometheusGrafanaDatadogGit