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AI Platform Engineering Lead
Skills
Continuous Integration And Continuous DeliveryAmazon EKSAmazon Web ServicesArtificial IntelligenceGraphics Processing UnitInfrastructure As CodeKubernetes
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Strong experience designing and operating Kubernetes platforms in production environments
- Hands-on experience administering and scaling Kubernetes clusters, ideally within AWS EKS
- Strong cloud engineering experience within AWS environments
- Experience building platform infrastructure from the ground up rather than solely supporting existing environments
- Strong understanding of Infrastructure as Code automation CI/CD monitoring and observability practices
- Experience working with distributed systems and high-performance compute environments
- Strong software engineering or scripting skills ideally using Python
- Experience supporting GPU-based workloads is highly desirable
- Exposure to ML infrastructure MLOps tooling training pipelines model deployment or inference platforms is advantageous
- Strong troubleshooting and problem-solving skills across infrastructure and platform environments
Nice to have
- Self-starter
- Collaborative
- Strategic thinker
- Problem solver
Day to day
- Design, build, and operate a scalable ML platform that supports the full machine learning lifecycle
- Develop and maintain Kubernetes-based infrastructure for AI, ML, and Generative AI workloads
- Build self-service capabilities that enable researchers and engineers to train, deploy, and manage models independently
- Support GPU-accelerated environments and help shape platform architecture, standards, and long-term strategy