Senior Developer Relations Manager - FSI

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, data science, or a related technical field—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 machine learning, 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 AI workloads across compute, memory, I/O, networking, latency, throughput, batching, GPU suitability, and cost-performance tradeoffs.
• Ability to lead technical and business discussions with engineering, platform, risk, and senior stakeholder teams.
• Excellent communication, stakeholder management, execution, and cross-functional collaboration skills.
• Working knowledge of NVIDIA AI technologies, including NIM, Triton, TensorRT, TensorRT-LLM, CUDA, Nsight, RAPIDS, NGC, NVIDIA AI Enterprise, and GPU Operator.
• Ability to apply NVIDIA technologies to fraud, risk, document AI, customer service, real-time decisioning, feature engineering, and large-scale analytics workloads.
• Ability to design secure, observable, compliant, and cost-efficient GPU-accelerated deployments across cloud, data-center, Kubernetes, and hybrid environments.
• Ways To Stand Out from the crowd: Experience in payments, fintech, banking technology, financial infrastructure, fraud platforms, compliance technology, or risk systems.
• Experience creating workload qualification frameworks, benchmark plans, or technical decision guides that help financial services developers decide when GPU acceleration is the right fit.
• Knowledge of fraud models, risk models, graph analytics, transaction intelligence, document AI, or real-time decisioning systems.
• Prior experience turning NVIDIA GPU computing, AI software, model serving, or acceleration libraries into regulated workload playbooks or production adoption plans .