Overview
Lead hands-on Developer Relations for India’s AI Labs to help researchers, ML infrastructure teams, and startup CTOs adopt NVIDIA platforms for AI model development and deployment.
What you'll do
- Build and execute a technical DevRel strategy to grow NVIDIA platform adoption across India AI Labs.
- Develop trusted relationships with founders, CTOs, researchers, infrastructure teams, platform leaders, and developer communities.
- Identify and accelerate high-value workloads (e.g., foundation model training, fine-tuning, RAG, multimodal AI, inference optimization, and production model serving).
- Profile AI Lab workloads to assess GPU acceleration fit and diagnose compute vs memory/I-O/network/orchestration bottlenecks.
- Build and adapt demos, code samples, notebooks, benchmark plans, reference architectures, and performance guides.
- Run workshops, code labs, architecture reviews, office hours, webinars, and executive briefings.
- Work hands-on with developers to debug integrations, profile workloads, improve inference performance, and capture feedback for product input.
What you'll need
- Bachelor’s degree in engineering/computer science/data science (or equivalent experience).
- 8+ years of experience in AI platforms, cloud infrastructure, fintech/payments/banking technology, data science, solution architecture, or developer ecosystems.
- Strong knowledge of ML, deep learning, generative AI, real-time inference, data engineering, MLOps, and cloud-native systems.
- Experience with regulated environments, high-availability systems, privacy/security/compliance, and production observability.
- Ability to profile across compute, memory, I/O, networking, latency, throughput, batching, GPU suitability, and cost-performance tradeoffs.
- Working knowledge of NVIDIA AI technologies including NIM, Triton, TensorRT, TensorRT-LLM, CUDA, Nsight, RAPIDS, NGC, NVIDIA AI Enterprise, and GPU Operator.
Nice to have
- Experience in payments/fintech/banking technology, fraud platforms, compliance technology, or risk systems.
- Experience creating workload qualification frameworks, benchmark plans, or technical decision guides for financial services developers.
- Knowledge of fraud/risk models, graph analytics, transaction intelligence, document AI, or real-time decisioning systems.
- Experience producing regulated workload playbooks or production adoption plans from NVIDIA GPU computing/serving/acceleration libraries.
