Senior Developer Relations Manager - Financial Services Industry

About the Role

NVIDIA is a leader in accelerated computing technologies driving innovations in generative AI, deep learning, data science, and cloud-native AI solutions. This position will spearhead developer relations efforts within India's AI Labs ecosystem, engaging with researchers, machine learning infrastructure teams, and startup CTOs to facilitate adoption of NVIDIA’s platforms targeting model development, training, optimization, deployment, and inference in production environments.

Key Responsibilities

• Formulate and implement a comprehensive Developer Relations strategy to enhance utilization of NVIDIA platforms across AI Labs in India.
• Establish and nurture trusted partnerships with founders, CTOs, ML researchers, infrastructure teams, platform leads, and developer communities.
• Identify and accelerate high-impact workloads such as foundation model training, speech AI, fine-tuning, retrieval augmented generation, multimodal AI, inference optimization, and model serving in production.
• Analyze AI Lab workloads to determine suitability for GPU acceleration by profiling bottlenecks and differentiating between compute, memory, I/O, network, or orchestration limitations.
• Create and customize technical demos, sample codebases, notebooks, benchmark strategies, reference architectures, and performance guidelines.
• Host advanced technical workshops, code labs, architecture critiques, office hours, webinars, and executive briefings to disseminate knowledge.
• Collaborate hands-on with developers resolving integration challenges, profiling workloads, enhancing inference speed, and selecting appropriate NVIDIA software stacks for specific applications.
• Clarify variations in model workload performance across labs caused by architectural, data pipeline, batch sizing, latency demands, infrastructure, software, or deployment factors.
• Gather and communicate developer feedback, technical obstacles, competitor intelligence, and product requirements back to NVIDIA’s product and engineering teams.

Qualifications and Skills

• Bachelor’s degree in engineering, computer science, data science, or related technical disciplines, or equivalent professional experience.
• A minimum of 8 years of relevant experience in AI platforms, cloud infrastructure, fintech, payments, banking technology, data science, solution architecture, or developer ecosystems.
• Deep understanding of machine learning, deep learning, generative AI, real-time inference, data engineering, MLOps, and cloud-native system architectures.
• Experience operating within regulated industries emphasizing high system availability, privacy, security, compliance, and production observability.
• Expertise in profiling AI workloads with respect to compute, memory, input/output, networking, latency, throughput, batching, GPU suitability, and analyzing cost-performance considerations.
• Proven ability to lead complex technical and business discussions involving engineering, platform, risk, and senior leadership stakeholders.
• Exceptional communication, stakeholder management, execution skills, and aptitude for cross-functional collaboration.
• Familiarity with NVIDIA AI technologies including NIM, Triton, TensorRT, TensorRT-LLM, CUDA, Nsight, RAPIDS, NGC, NVIDIA AI Enterprise, and GPU Operator.
• Capability to leverage NVIDIA tech for domains such as fraud detection, risk management, document AI, customer service, real-time decisioning, feature engineering, and large-scale analytics workloads.
• Ability to architect secure, observable, compliant, and cost-effective GPU-accelerated solutions across cloud platforms, data centers, Kubernetes, and hybrid infrastructures.

Preferred Attributes

• Experience in payments, fintech, banking technology, financial infrastructure, fraud detection platforms, compliance technology, or risk management systems.
• Practice developing workload qualification frameworks, benchmarking methodologies, or technical decision-making guides to assist financial services developers in evaluating GPU acceleration suitability.
• Knowledge in fraud analytics, risk modeling, graph analytics, transaction intelligence, document AI, or real-time decisioning architectures.
• Previous roles involving translation of NVIDIA GPU computing, AI software, model serving, or acceleration libraries into regulated workload playbooks and production adoption strategies.

Additional Details

Reference Code: JR2023131