Software Engineer
Flipkart · Bengaluru
- Experience5–9 yrs
- SalaryNot disclosed
- Work modeonsite
- Levelsenior
- Posted2 Sept 2026
About Flipkart
Flipkart is hiring in Bengaluru in ecommerce retail. This role looks for around 5+ years of experience.
Skills
- Computer Science
- Engineering
- Kubernetes
- Go
- Python
- Kubebuilder
- Operator SDK
- controller-runtime
- Kubernetes API server
- etcd
- Kubernetes scheduler
- Kubernetes controller manager
- kubelet
- Gateway API
- CNI plugins
- service load balancing
- hybrid cloud architectures
- NVIDIA Network Operator
- SR-IOV device plugins
- RDMA
- NCCL
- Kubernetes GPU scheduler frameworks
- MIG partitioning
- KubeRay
- Ray
- bare-metal provisioning
- Argo CD
- NVIDIA CUDA
- NVLink
- NVSwitch
- GPU Operator
- Prometheus
- Grafana
- OpenTelemetry
The role
A site reliability and platform engineer at an e-commerce marketplace builds distributed Kubernetes infrastructure for GPU workloads, specializing in Kubernetes internals and GPU computing, and applies Go, Python, and GitOps.
Full job description
Requirements:
Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
5+ years of experience building distributed systems or infrastructure platforms with deep Kubernetes expertise.
Strong programming skills in Go and/or Python.
Familiarity with Kubernetes controller development frameworks such as Kubebuilder, Operator SDK, or controller-runtime.
Deep understanding of Kubernetes internals, including the API server, etcd, scheduler, controller manager, and kubelet.
Hands-on experience designing Kubernetes networking, including Gateway API, CNI plugins, service load balancing, and hybrid cloud architectures.
Experience designing and operating multi-NIC Kubernetes clusters using NVIDIA Network Operator, SR-IOV device plugins, or equivalent tooling.
Strong understanding of RDMA networking protocols and NCCL configuration for distributed GPU workloads.
Experience with Kubernetes GPU scheduler frameworks and GPU pool management, including MIG partitioning and preemption policies.
Hands-on experience deploying and operating KubeRay for distributed Ray workloads.
Experience with GPU asset lifecycle management, bare-metal provisioning automation, and GitOps-based CD tooling such as Argo CD.
Familiarity with GPU technologies, including NVIDIA CUDA, NVLink, NVSwitch, device plugins, and the GPU Operator ecosystem.
Experience with observability tooling such as Prometheus, Grafana, and OpenTelemetry.
Strong debugging and performance optimisation skills across GPU driver stacks, RDMA networking, and distributed Kubernetes infrastructure.