Senior Developer Relations Manager - Conglomerates

Overview

• NVIDIA is the leading full-stack accelerated computing company, powering the next wave of generative AI, agentic AI, deep learning, data science, cloud-native AI, and edge AI.
• This role will lead hands-on Developer Relations with India's AI Labs, helping researchers, ML infrastructure teams, and startup CTOs adopt NVIDIA platforms for model development, training, optimization, deployment, and production inference.
• The ideal candidate is a senior technical DevRel leader who can earn credibility with ML researchers and platform engineers.
• This person should be comfortable reading code, writing examples, building demos, running benchmarks, explaining architecture tradeoffs, profiling workloads, and translating developer feedback into useful product input.
• What You'll Be Doing: Build and execute a technical Developer Relations strategy to grow NVIDIA platform adoption across AI Labs in India.
• Develop trusted relationships with founders, CTOs, ML researchers, ML infrastructure teams, platform leaders, and developer communities.
• Identify and accelerate high-value workloads such as foundation model training, fine-tuning, speech AI, retrieval augmented generation, multimodal AI, inference optimization, and production model serving.
• Assess which AI Lab workloads are a strong fit for GPU acceleration by profiling bottlenecks and distinguishing compute-bound problems from memory-bound, IO-bound, network-bound, or orchestration-bound issues.
• Build and adapt technical demos, sample code, notebooks, benchmark plans, reference architectures, and performance guides.
• Run deep technical workshops, code labs, architecture reviews, office hours, developer sessions, technical webinars, and executive briefings.
• Work hands-on with developers to debug integration issues, profile workloads, improve inference performance, and identify the right NVIDIA software stack for each use case.
• Explain why similar model workloads may perform differently across labs due to model architecture, data pipeline design, batch size, latency targets, storage/network behavior, software stack, or deployment environment.
• Capture developer feedback, technical blockers, competitive insights, and product requirements for NVIDIA product and engineering teams.
• What We Need To See: Bachelor’s degree in engineering, computer science, business technology, or a related technical discipline—or equivalent experience.
• 10+ years of experience in strategic partnerships, enterprise technology, solution architecture, technical sales, business development, product management, or developer ecosystems.
• Strong knowledge of enterprise and private AI, cloud infrastructure, data platforms, modern application architecture, and AI deployment lifecycles.
• Ability to engage senior executives and hands-on engineering or architecture teams with equal credibility.
• Experience working across complex, matrixed organizations involving sales, technical, product, marketing, and partner teams.
• Strong commercial judgment, structured account planning, and the ability to translate technical capabilities into measurable business outcomes.
• Excellent communication, stakeholder management, cross-functional collaboration, and execution skills.
• Working knowledge of NVIDIA AI Enterprise, NIM, Triton, TensorRT, TensorRT-LLM, CUDA, NGC, GPU Operator, and cloud and data-center deployment patterns.
• Ability to position NVIDIA technologies across private AI, industrial AI, digital twins, simulation, accelerated analytics, computer vision, and enterprise AI factories.
• Familiarity with Omniverse, Isaac Sim, and synthetic-data workflows, with the ability to map NVIDIA platform components to business workloads and explain implementation paths to executive and technical audiences.
• Ways To Stand Out from the Crowd: Experience with large conglomerates or global strategic enterprise accounts.
• Knowledge of industrial AI, manufacturing systems, automotive technology, telecom infrastructure, digital twins, simulation, accelerated analytics, or private AI.
• Prior experience shaping executive or business-unit adoption plans around NVIDIA AI, data center, simulation, edge, or enterprise software platforms.
• Ability to create lighthouse use cases that can scale across multiple business units.