Engagement Type: This is initially a contractual position, with the potential to transition into a permanent, full-time role based on the candidate’s technical capabilities, performance, and mutual fit with NEXUS AIDC’s long-term engineering objectives.

About Company: NEXUS AIDC is advancing the next generation of intelligent data center operations through its AI-native Data Center Operating System. Our unified platform connects physical infrastructure, GPU compute, AI workloads, and operational intelligence, enabling real-time visibility, predictive insights, resource optimization, and intelligent automation.As we expand our enterprise footprint, we continue to develop advanced platform capabilities to support increasingly complex AI infrastructure and high-performance computing environments.

Chance: We are seeking a technically strong, hands-on Senior AI Infrastructure Platform Engineer with 5+ years of experience in Kubernetes, NVIDIA GPU infrastructure, cloud-native software development, and AI workload management.The ideal candidate will combine strong software engineering expertise with practical experience in GPU cluster architecture, Kubernetes scheduling, workload orchestration, and enterprise platform integration.This role requires the ability to design and develop production-grade platform services, evaluate and integrate advanced NVIDIA technologies, and independently implement, test, and optimize complex AI infrastructure solutions.

We are particularly interested in engineers who have moved beyond traditional infrastructure deployment and administration to developing Kubernetes-native controllers, operators, scheduling systems, APIs, and infrastructure automation platforms.

Responsibilities

- Design and develop scalable AI infrastructure management services, GPU resource optimization, workload management, and intelligent automation capabilities.
- Architect, deploy, configure, and troubleshoot Kubernetes-based NVIDIA GPU infrastructure, including B200 and other enterprise GPU platforms.
- Develop Kubernetes-native controllers, operators, scheduling components, APIs, and automation services for GPU resource allocation and workload lifecycle management.
- Evaluate, test, and integrate NVIDIA Run and comparable GPU orchestration technologies within the NEXUS ecosystem.
- Implement GPU scheduling, workload queues, prioritization, resource quotas, multi-tenancy, and capacity optimization.
- Integrate NVIDIA GPU Operator, CUDA,



GPU drivers, container runtimes, and supporting infrastructure technologies.
- Deploy, validate, and optimize AI training, fine-tuning, inference, and distributed computing workloads.
- Diagnose GPU performance bottlenecks, resource contention, distributed training issues, and high-performance networking dependencies.
- Develop integrations for GPU telemetry, infrastructure monitoring, workload orchestration APIs, and observability systems.
- Conduct functional, performance, scalability, and reliability testing to support enterprise deployments and customer acceptance.
- Collaborate with Product, DevOps, and Engineering teams on platform architecture, integration frameworks, and continuous product enhancement.

Required Skills & Experience

- 5+ years of hands-on experience in Kubernetes, GPU infrastructure, cloud-native platform engineering, or distributed systems development.
- Strong programming expertise in Python, Go, or C/C++, with demonstrated experience building production-grade services, APIs, integrations, and infrastructure automation.
- Practical experience evaluating, deploying, integrating, or developing solutions using NVIDIA Run or comparable GPU orchestration technologies.
- Demonstrated experience deploying, configuring, and troubleshooting NVIDIA GPU clusters in Kubernetes environments.
- Deep understanding of Kubernetes architecture, controllers, operators, scheduling, Helm, container runtimes, and workload lifecycle management.
- Strong knowledge of GPU scheduling, resource allocation, workload queues, multi-tenancy, and orchestration frameworks.
- Working knowledge of NVIDIA GPU Operator, CUDA, GPU drivers, and associated infrastructure software.
- Familiarity with PyTorch, TensorFlow, distributed AI training, fine-tuning, and inference workloads.
- Experience with NVIDIA DCGM, Prometheus, Grafana, or comparable GPU telemetry and observability technologies.
- Strong Linux, networking, performance optimization, and complex infrastructure troubleshooting capabilities.




- Experience developing REST APIs and integrating enterprise applications, databases, telemetry pipelines, and configuration management systems.
- Familiarity with DevOps practices, CI/CD, Infrastructure as Code, Terraform, Ansible, and hybrid cloud environments.
- Ability to independently design, develop, implement, and validate complex technical solutions in a product engineering environment.
- Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent practical experience.

Preferred Skills

- Hands-on experience with NVIDIA DGX/HGX B200, Blackwell GPU infrastructure, or production H100/H200 clusters.
- Knowledge of NVLink, NVSwitch, NVIDIA Fabric Manager, InfiniBand, RoCE, and GPUDirect RDMA.
- Experience with NVIDIA MIG, GPU partitioning, resource isolation, and multi-tenant GPU environments.
- Familiarity with distributed training frameworks, including NCCL and PyTorch Distributed.
- Experience with KServe, NVIDIA Triton Inference Server, vLLM, Kubeflow, Slurm, or Volcano.
- Experience developing Kubernetes Custom Resource Definitions (CRDs), custom scheduling logic, controllers, or infrastructure management platforms.
- Familiarity with NVIDIA Nsight, GPU performance profiling, high-performance computing, and infrastructure capacity optimization.

Why Join NEXUS: Join a growing AI infrastructure technology company advancing intelligent and autonomous data center operations. Work alongside an experienced engineering team on sophisticated, real-world NVIDIA GPU and AI infrastructure challenges, contributing directly to the development of an enterprise-grade AI-native platform.

This opportunity offers meaningful technical ownership across software engineering, GPU infrastructure, workload management, platform integrations, and intelligent automation.

How to Apply: Interested candidates must apply through the LinkedIn job posting and submit an updated resume highlighting relevant experience in Kubernetes platform development, NVIDIA GPU infrastructure, software engineering, and AI workload orchestration.

Candidates with demonstrable experience developing Kubernetes-native platform components and independently implementing GPU infrastructure solutions will be prioritized.

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📌 Senior NVIDIA GPU & AI Infrastructure Platform Engineer | Immediate Joiners Preferred (Bengaluru)
🏢 nexus aidc
📍 Bengaluru