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.

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 solid 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, data science, or a related technical discipline, or equivalent experience and 8+ years of experience in technical Developer Relations, ML engineering, AI platforms, model infrastructure, cloud platforms, solution architecture, or startup technical engagement.
- Strong understanding of generative AI, deep learning, machine learning, model lifecycle, inference serving, model optimization, distributed training, and MLOps.
- Hands-on programming experience in Python, with working familiarity in C++, CUDA, or other performance-oriented development environments preferred.- Ability to profile and reason about AI workloads, including GPU suitability, compute intensity, memory bandwidth, IO bottlenecks, network constraints, latency, throughput, batching, and cost/performance tradeoffs.
- Ability to engage both deeply technical developers and senior business stakeholders, with credibility in whiteboarding, live demos, technical troubleshooting, and architecture reviews.
- Hands-on exposure to core NVIDIA AI software and SDKs relevant to model builders,



including NeMo Framework, NeMo Curator, Nemotron model families, TensorRT-LLM, NVIDIA NIM, Triton Inference Server, CUDA, NCCL, Nsight Systems, Nsight Compute, NGC containers, and NVIDIA AI Enterprise.
- Ability to demonstrate how NeMo Framework supports model training, customization, alignment, evaluation, and deployment workflows for LLMs, multimodal models, and speech AI.
- Ability to reason about when NeMo Curator or related data curation workflows can improve training data quality through filtering, deduplication, formatting, synthetic data workflows, and multimodal data preparation.
- Ability to build or adapt demos that show how Nemotron models, NIM microservices, TensorRT-LLM, and Triton can move a model from experimentation to optimized inference.

Ways To Stand Out from the crowd :

- Hands-on experience with large-scale model training, fine-tuning, inference optimization, or AI platform engineering.
- Hands-on experience with inference optimization and model efficiency techniques such as quantization, distillation, pruning, speculative decoding, batching, KV cache optimization, RLHF, RLAIF, DPO, or other post-training and alignment methods.
- Experience creating workload qualification frameworks, benchmark plans, or technical decision guides that help developers decide when GPU acceleration is the right fit.
- Track record as a technical DevRel practitioner, including public talks, workshops, GitHub samples, blogs, tutorials, reference architectures, or developer community programs.
- Strong point of view on India's sovereign AI, Indic language AI, and foundation model ecosystem.

📌 Senior Developer Relations Manager (Bengaluru)
🏢 NVIDIA
📍 Bengaluru