Senior ML Ops Engineer

KAYAK

. Build and maintain ML infrastructure end-to-end: Extend and operate the infrastructure that powers every model we ship — including CI/CD pipelines, model orchestration, and automated training pipelines designed to scale reliably without manual intervention.

Posted 7/3/2026full-time🇩🇪 GermanySeniorWebsite

Tech Stack

Tools & technologies

DockerGrafanaKubernetesLinuxPrometheusPython

About the role

Key responsibilities & impact

Build and maintain ML infrastructure end-to-end: Extend and operate the infrastructure that powers every model we ship — including CI/CD pipelines, model orchestration, and automated training pipelines designed to scale reliably without manual intervention.
Own model deployment and serving: Help define and evolve the standards and tooling for model serving, ensuring low latency and high availability across our ML services.
Develop core MLOps capabilities: Establish and maintain essential infrastructure that functions as reliable, self-service systems for the entire machine learning organization — with a focus on feature stores, model registries, and automated monitoring for performance and data drift.
Operationalize infrastructure for the ML team: Collaborate with Operations to enable Kubernetes (k8s) autoscaling and GPU provisioning, turning these into accessible, self-service tools for ML practitioners — including standing up and operating a Kubernetes-based development cluster and taking models from experimentation to GPU-backed production.
Improve platform reliability and performance: Partner with Operations to design resilient monitoring using advanced observability tooling. Define service-level objectives and implement automation to reduce manual interventions and improve system reliability.
Empower Data Scientists through standardized, optimized workflows: Amplify the impact of the ML team by building clear, well-supported "golden paths" — standardized workflows that streamline the model development lifecycle and let Data Scientists focus on modeling while you handle the infrastructure.

Requirements

What you’ll need

Experience building and operating ML platforms in production environments.
Solid working knowledge of containerization and orchestration (Docker, Kubernetes), Linux internals, and model serving at scale.
Familiarity with ML lifecycle tooling, including orchestration frameworks, feature stores, model registries, and drift or performance monitoring.
Experience owning production systems: defining service-level objectives (SLOs), building observability (for example, using tools such as Prometheus, Grafana, or Datadog), participating in incident response, and diagnosing large-scale failures systematically. You look for opportunities to automate repetitive work rather than absorb it.
Comfort writing production-quality code in Python or a comparable language.
Experience modernizing production infrastructure with attention to reliability, risk, and cost — including thoughtful sequencing of work to maintain availability and continuity for live systems.
The ability to take ownership of technical outcomes, advocate for decisions using data, and communicate clearly in writing and in person — to both technical and non-technical audiences.

Benefits

Comp & perks

Work from (almost) anywhere for up to 20 days per year
Focus on mental health and well-being:
Company-paid therapy sessions through SpringHealth
Company-paid subscription to HeadSpace
Company-wide week off a year – the whole team fully recharges (and returns without a pile-up of work!)
No meeting Fridays
Paid parental leave
Paid volunteer time
Focus on your career growth:
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
An awesome office in Friedrichshain, Berlin

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Hard Skills & Tools

ML InfrastructureCI/CD PipelinesModel OrchestrationContainerizationOrchestration FrameworksFeature StoresModel RegistriesPerformance MonitoringService-Level Objectives (SLOs)Production-Quality Code

Soft Skills

Clear CommunicationOwnership of Technical OutcomesAdvocacy Using Data