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.
