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.