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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing, building, and operating distributed AI production systems at scale, with a strong focus on cloud platforms like AWS and GCP. Proficient in leveraging modern AI coding tools and implementing CI/CD practices to enhance system reliability and performance.
Highest-signal resume keywords
DevOps ExperienceCloud Infrastructure ExpertiseKubernetes and Helm ProficiencyAI Workload ManagementCI/CD and Automation Skills
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
DevOpsDistributed SystemsAI Factory SystemsGPU InferencingInfrastructure as CodeKubernetesHelmCI/CDNetworkingOperating Systems
Soft Skills
Project LeadershipCollaboration
Tools & Technologies
AWSGCPDockerTerraformGitHub ActionsPrometheusGrafanaKubeFlowRayAI Coding Tools
Industry Keywords
AI Production SystemsObservabilityMonitoringCloud Inferencing ServicesAI Evals
Tech Stack
Tools & technologiesAWSCloudDistributed SystemsDNSDockerGoogle Cloud PlatformGrafanaKubernetesLinuxPrometheusRayTCP/IPTerraform
About the role
Key responsibilities & impact- Join a global DevOps & Platform team responsible for designing and operating distributed AI production systems at very high scale
- Own projects end-to-end, from design through implementation to production rollout
- Design, build, and operate highly available distributed systems at scale on AWS and GCP
- Work across Kubernetes, Docker, Helm, CI/CD, automation, data, caching, and messaging layers
- Modernize and consolidate different stacks and architectures into Kaltura's single coherent platform
- Build and operate infrastructure for AI workloads, including GPU inference and serving platforms
- Drive system reliability, monitoring, and observability
- Troubleshoot complex issues across application, infrastructure, networking, operating system, and data layers
- Collaborate with Platform, Development, Architecture, AI/ML Research, and Product teams
- Leverage modern AI coding tools to work at high velocity and quality
Requirements
What you’ll need- 2+ years of hands-on experience in DevOps / Platform / SRE roles, operating distributed production systems at large scale, with a proven ability to lead projects independently and drive new implementations end-to-end
- 2+ years of experience working with cloud Inferencing services and NeoClouds
- 2+ years of hands-on experience building and maintaining AI Factory systems on KubeFlow/Ray/similar
- 2+ years of hands on experience with GPU Inferencing (vLLM/SGLang/Nvidia Triton)
- Deep hands-on expertise with multicloud AWS/GCP/NeoClouds (RunPod, TogetherAI, vast.ai) including Infrastructure as Code (Terraform or similar)
- Deep hands-on expertise with Kubernetes and Helm, including a strong understanding of how Kubernetes internals work and how to operate them in production, alongside Docker / containerized systems
- Strong knowledge of networking and operating systems (TCP/IP, DNS, load balancing, Linux internals)
- Strong experience with CI/CD and automation (GitHub Actions), monitoring and observability (metrics, tracing, logging, tools like Prometheus / Grafana)
- High proficiency with current AI coding tools, including a deep understanding of how they work and how to apply them to drive real productivity
- Familiarity with AI evals and AI observability systems, such as Opik, OpenEval, LangSmith etc. (nice to have)
Benefits
Comp & perks- Hybrid, flexible work environment
- Extended private health (including mental) insurance
- Personal and professional development programs
- Occasional Cross company long weekends