• Design, build, and operate the compute orchestration layer powering a GPU-native cloud platform for AI and high-performance workloads. • Maintain the existing CloudStack code base used in current production deployments. • Integrate new upstream CloudStack releases into the internal platform fork. • Perform upgrades of existing customer environments to newer CloudStack versions. • Design and execute safe upgrade paths for running production environments. • Troubleshoot orchestration and provisioning issues in existing deployments. • Maintain and troubleshoot CloudStack VPC networking. • Work with and understand CloudStack Debian VPC routers. • Manage networking implementations based on Open vSwitch (OVS) and OVN. • Improve the reliability of network orchestration components. • Manage hypervisor implementations based on KVM and QEMU. • Maintain and evolve the code responsible for QEMU GPU passthrough, including PCI mapping and exposure of L40S, RTX 6000 Pro, and H200 GPUs to virtual machines. • Design orchestration and scheduling primitives for the next-generation platform based on Kubernetes, Slurm, and Argo Workflows. • Build orchestration workflows that expose GPU and CPU compute resources to platform users. • Implement Kubernetes scheduling strategies including GPU partitioning, Multi-GPU job placement, and Topology-aware scheduling for distributed training and inference. • Design and implement Kubernetes-based GPU/CPU scheduling infrastructure for multi-tenant AI workloads. • Design and operate Slurm-based HPC scheduling environments integrated with Kubernetes clusters. • Implement support for Multi-node distributed GPU training, Gang scheduling, and build automation for Dynamic Slurm node registration. • Design and implement workflow orchestration using Argo Workflows and develop reusable workflow templates for common platform workloads.

• Lead the design, implementation, and evolution of our cloud-native platform infrastructure. • Build and maintain scalable, resilient systems that empower our engineering teams to deliver innovative solutions rapidly and reliably. • Design, deploy, and manage scalable cloud solutions on AWS public cloud platform via Infrastructure as Code. • Manage infrastructure as code (IaC) leveraging Terraform, CloudFormation and GoLang. • Design and implement Kubernetes-based platform solutions with focus on scalability, reliability, and security. • Support and maintain large Kubernetes clusters in production environments. • Implement security best practices and ensure compliance with industry standards and regulations. • Work closely with development, operations, and security teams to integrate infrastructure as code practices. • Develop automation to build and deploy Docker Containers through CI/CD pipelines for engineering teams deploy and test services. • Write policy & standard validation tests and integrations with Security Scanning software to ensure compliance. • Implement and support Observability solutions to ensure platform performance, reliability, and scalability. • Create Dashboards and integrate into Backstage IDP for visibility into system health. • Provide guidance and mentorship to team members on best practices in GitOps, CI/CD, and infrastructure management.

• Design, develop, deploy and support modules of large world-class enterprise-level product. • Participate in architectural design of product. • Develop high level development timelines based on project scope and understanding of the existing application code. • Evaluate new design specifications and raise quality standards, address architectural concerns. • Evaluate stability, compatibility, scalability, interoperability, and performance of the software product. • Maintain and upgrade product source codes. • Demonstrate technical expertise through publication, presentations, white papers and event participation. • Continually learn new technologies in related areas. • Serve as a source of technical expertise and mentor junior team members.

• Responsible for designing and implementing complex AI components — from initial concept to production • Enhance and scale existing AI solutions to improve performance and efficiency • Develop robust data pipelines, backends, and API interfaces for seamless integration • Build and maintain automated deployment pipelines to streamline releases and testing • Ensure quality and performance of backend processes and NLP algorithms through continuous optimization • Work closely with the product team to translate data-driven insights into market-ready, innovative solutions