[Remote] Sr. Implementation Engineer (Kubernetes/AI)
Note: The job is a remote job and is open to candidates in USA. Rafay is seeking a highly motivated, customer-focused, and technically skilled Sr. Implementation Engineer to join our growing Services team. In this role, you will partner with Rafay’s enterprise customers to architect, deploy, and optimize cloud-native infrastructure solutions leveraging the Rafay platform and modern Kubernetes ecosystems.
Responsibilities
- Serve as the primary technical lead for customer implementation and deployment engagements
- Partner with customers to gather requirements, conduct architecture and design reviews, define project scope, and implement scalable solutions
- Design, deploy, and configure Rafay platform capabilities across public cloud, private cloud, and hybrid environments
- Troubleshoot complex infrastructure, networking, Kubernetes, virtualization, and cloud-related issues; identify root causes and recommend remediation strategies
- Collaborate with Customer Success, Product, and Engineering teams to resolve technical challenges and drive customer success
- Respond to customer issues within established Service Level Agreements (SLAs) while effectively managing customer expectations and business impact
- Reproduce customer-reported issues, perform detailed technical analysis, and communicate product defects and enhancement requests to Engineering and Product teams
- Act as a customer advocate by driving issue resolution and escalating critical concerns when necessary
- Develop and maintain technical documentation, implementation guides, deployment runbooks, and knowledge base articles
- Provide technical mentorship to junior engineers and contribute to implementation best practices and process improvements
- Stay current on Rafay platform capabilities, Kubernetes technologies, cloud-native ecosystems, and emerging industry trends
- Work independently and collaboratively in a fast-paced, customer-focused environment
Skills
- 10+ years of experience in customer-facing technical roles, including implementation, solutions engineering, professional services, or technical consulting
- Hands-on experience in AI/ML engineering, including Agentic AI systems, model inference, model deployment, training workflows, and the design and management of ML pipelines
- Strong experience designing and implementing solutions in Kubernetes and cloud-native environments
- Excellent written and verbal communication skills with the ability to communicate complex technical concepts to both technical and non-technical audiences
- Deep troubleshooting expertise across networking, virtualization platforms (VMware vSphere, OpenStack), container orchestration platforms (Kubernetes), and public cloud environments (AWS, Microsoft Azure, Google Cloud Platform)
- Strong understanding of Linux systems administration, networking protocols, security concepts, and distributed systems
- Proven ability to lead technical implementation projects and manage multiple customer engagements simultaneously
- Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field; equivalent practical experience will also be considered
- Experience with infrastructure automation, Infrastructure as Code (Terraform preferred), and CI/CD workflows is highly desirable
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