Top Tech Jobs & Startup Jobs in Pune

Develop platforms and software for Media & Entertainment and Broadcasting, focusing on Kubernetes deployment, networking, and automation within the GPU Software team at NVIDIA.

Lead major incident response and set technical direction for site reliability engineering across NVIDIA’s enterprise platforms. Design and operate distributed, Kubernetes-based cloud infrastructure; build automation, observability, self-healing systems, and AI-assisted incident tooling. Drive root cause analysis, SLOs, error budgets, and systemic reliability improvements. Partner with Cloud, Platform, Security, and AI/ML teams, mentor engineers, influence architecture, and communicate with executives during critical incidents.

Support enterprise customers deploying NVIDIA AI Enterprise across cloud and datacenter environments. Troubleshoot complex software issues, reproduce failures, collect diagnostics, and partner with engineering on fixes. Build Python automation, diagnostics, test harnesses, Kubernetes deployment assets, patches, documentation, and runbooks. Work with Linux, containers, GPUs, AI frameworks, inference services, distributed systems, and cloud platforms. Own customer escalations through resolution and participate in a monthly weekend Sev1 on-call rotation.

Lead NVIDIA’s graphics systems team in developing and productionizing an agentic software engineering framework. Responsibilities include defining strategy and transformation roadmaps, applying agents across requirements, implementation, validation, review, and release, maintaining graphics and display product commitments, growing engineering talent, and packaging the resulting orchestration, tools, evaluations, and operating practices for adoption across NVIDIA.

Develop and automate tests for NVIDIA’s embedded, GPU-based, and robotics software and hardware stack. Responsibilities include building test strategies, improving code coverage, debugging regressions, creating CI/CD workflows, developing AI-driven testing agents, evaluating model performance, and reporting quality metrics. The role collaborates with hardware, software, release, and project teams while supporting robotics simulation, perception, navigation, manipulation, and functional safety validation.

Lead operational readiness for NVIDIA Cloud Partners running large-scale GPU infrastructure. Build continuous validation, observability, automated detection and remediation, fleet lifecycle management, configuration management, and operational frameworks across Linux, Kubernetes, networking, storage, and AI workloads. Define health signals, SLOs, readiness criteria, runbooks, and reusable automation while collaborating directly with partner engineering and operations teams.

Develop and deploy large-scale AI solutions, distributed training and inference systems, and MLOps pipelines on cloud and NVIDIA platforms. Advise customers, diagnose full-stack machine learning issues, optimize performance, latency, cost, and reliability, support new hardware in open-source frameworks, and create open-source tools and reference architectures. Collaborate with infrastructure and accelerated-framework teams while communicating technical architectures and recommendations to engineering and leadership audiences.

Design, build, and optimize high-performance full-stack applications for web, desktop, mobile, and TV platforms. Develop responsive native and web interfaces using Angular, React, JavaScript, and related technologies; consume RESTful APIs; improve reliability, scalability, security, and cross-platform performance; create CI scripts; and collaborate with product, design, operations, and architecture teams.

Build and optimize high-performance local AI inference software for NVIDIA RTX and DGX GPUs. Develop inference runtimes and execution stacks, optimize models and data pipelines using quantization, pruning, sparsity, and distillation, and perform system-level debugging and performance analysis. Collaborate with software, research, architecture, and product teams to improve scalability, stability, accuracy, and production readiness across language, vision, speech, and diffusion workloads.

Design, develop, and deploy AI-powered automation for hardware performance verification. Build intelligent tools and agents for test generation, regression detection, failure analysis, and corrective recommendations. Integrate LLM assistants, create verification data pipelines, establish impact dashboards, and collaborate with verification engineers. Apply generative AI, reinforcement learning, and formal methods to improve coverage, reduce cycle times, and increase productivity across complex GPU, CPU, memory, and HPC verification workflows.