Top Tech Jobs & Startup Jobs in Mumbai

Architect and deliver generative AI solutions centered on LLM training, optimization, deployment, and RAG workflows. The role involves customer collaboration, technical workshops, pre-sales presentations, solution integration, and technical leadership. Responsibilities include fine-tuning models, optimizing GPU-based inference, designing scalable training workflows, and partnering with NVIDIA engineering teams. Candidates need deep expertise in language models, TensorFlow, PyTorch, Hugging Face, GPU clusters, and production deployments, with cloud, containerization, and Kubernetes experience preferred.

Supports and operates NVIDIA’s large-scale on-premises and cloud network infrastructure in a 24/7 global operations environment. Responsibilities include monitoring alerts, resolving incidents within SLAs, troubleshooting network issues, managing IP infrastructure and datacenter interconnects, coordinating vendors, implementing upgrades and capacity expansions, improving operational workflows, and documenting best practices. The role requires extensive networking protocol knowledge, cloud experience, automation exposure, and strong incident-management and communication skills.

Lead strategic partner engagement with major Indian conglomerates to identify, design, and scale enterprise AI workloads. Build executive and technical relationships, coordinate cross-functional NVIDIA teams, run workshops and architecture reviews, develop repeatable use cases, and capture account requirements and product feedback to deliver deployable, scalable AI outcomes across cloud, data center, edge, and industrial environments.

Lead technical Developer Relations for NVIDIA across AI Labs in India. Build platform adoption through developer relationships, technical workshops, demos, benchmarks, architecture reviews, and performance optimization. Assess AI workloads for GPU acceleration, debug integrations, recommend NVIDIA software stacks, and translate developer feedback into product requirements. Engage executives, researchers, infrastructure teams, and startup CTOs while coordinating with sales, product, engineering, marketing, and partners.

Architect end-to-end generative AI solutions centered on LLM training, fine-tuning, deployment, inference optimization, and RAG workflows. Collaborate with customers, partners, marketing, business development, and engineering teams to define tailored solutions. Lead technical workshops, training sessions, and design engagements while providing guidance on GPU-accelerated, cloud, on-premises, and scalable distributed deployments.

Leads technical Developer Relations for NVIDIA AI platforms across India's financial services AI labs. Builds adoption strategies, technical demos, benchmarks, reference architectures, workshops, and performance guides. Works directly with researchers, infrastructure teams, and CTOs to profile workloads, optimize inference, debug integrations, and design secure, observable GPU deployments across cloud, data centers, Kubernetes, and hybrid environments. Translates developer feedback and workload requirements into product and engineering input.

Architects, implements, and supports advanced SAP IBP and APO supply chain planning solutions. Leads process improvements, prototypes, testing, upgrades, training, data analysis, and optimization modeling for supply and demand planning. Collaborates with business and IT teams to deliver scalable solutions, improve profitability, and support semiconductor industry planning needs. The role also mentors stakeholders and influences senior leadership on system adoption and operational improvements.

Design and ship Java, Go, and Rust services for NVIDIA Cloud Functions, a distributed platform routing AI workloads across GPU fleets. Improve performance, reliability, scalability, and cloud-native build and release processes. Collaborate across NVIDIA technologies, contribute to an open-source project, triage community issues, review pull requests, and write documentation. The role requires expertise in systems programming, distributed architecture, Kubernetes, containerization, Linux internals, scripting, and continuous integration.

Own and resolve complex Slurm support cases for production AI and HPC clusters. Diagnose scheduler, Linux, networking, storage, authentication, database, container, and GPU infrastructure issues; advise customers on configuration, upgrades, reliability, and incident recovery; collaborate with engineering on defects; and develop technical documentation, diagnostic tools, and training.

Design, develop, operate, and optimize large-scale software-defined networking solutions for NVIDIA AI Cloud environments. Responsibilities include building OVS/OVN control and data plane software, Kubernetes networking services, observability systems, CI/CD pipelines, GitOps integrations, and secure gRPC/REST services. The role owns production reliability, incident response, performance tuning, and upstream open-source contributions while collaborating with SRE, DevOps, and network engineering teams.