Senior Developer Relations Manager - Financial Services Industry

Overview

NVIDIA is a leader in full-stack accelerated computing, driving advancements in generative AI, agentic AI, deep learning, data science, cloud-native, and edge AI. This position focuses on leading Developer Relations with AI Labs across India, assisting researchers, ML infrastructure teams, and startup CTOs in adopting NVIDIA platforms to advance model development, training, optimization, deployment, and inference in production environments.

Key Responsibilities

• Develop and implement a technical Developer Relations strategy targeting growth of NVIDIA platform use within India's AI Labs.
• Build strong, trusted collaborations with founders, CTOs, machine learning researchers, infrastructure teams, platform leaders, and developer communities.
• Identify and accelerate high-value AI workloads such as foundation model training, fine-tuning, speech AI, retrieval-augmented generation, multimodal AI, inference optimization, and production model serving.
• Analyze AI workloads to evaluate GPU acceleration suitability by profiling bottlenecks differentiating compute-bound from memory, I/O, network, or orchestration constraints.
• Create and customize technical demos, sample code, notebooks, benchmark strategies, reference architectures, and performance documentation.
• Conduct technical workshops, code labs, architecture reviews, developer sessions, webinars, office hours, and executive briefings.
• Support developers hands-on in debugging integration challenges, profiling workloads, enhancing inference performance, and selecting appropriate NVIDIA software stacks.
• Clarify performance variances of similar AI models due to differences in architecture, data pipelines, batch sizes, latency, storage/network setup, software stacks, or deployment environments.
• Collect developer feedback, identify technical barriers, gather competitive intelligence, and relay product requirements to NVIDIA's product and engineering teams.

Candidate Profile

• Bachelor’s degree or equivalent in engineering, computer science, data science, or related technical disciplines.
• At least 8 years’ experience in AI platforms, cloud infrastructure, fintech, payments, banking tech, data science, solution architecture, or developer ecosystems.
• Comprehensive knowledge of machine learning, deep learning, generative AI, real-time inference, data engineering, MLOps, and cloud-native architectures.
• Familiarity with regulated environments emphasizing high availability, privacy, security, compliance, and production observability.
• Expertise profiling AI workloads considering compute, memory, I/O, networking, latency, batching, GPU suitability, and cost-performance balance.
• Strong ability to lead technical and business discussions with engineering teams, platform teams, risk divisions, and senior stakeholders.
• Excellent communication, stakeholder management, execution skills, and ability to collaborate cross-functionally.
• Hands-on experience with NVIDIA AI technologies such as NIM, Triton, TensorRT (including TensorRT-LLM), CUDA, Nsight, RAPIDS, NGC, NVIDIA AI Enterprise, and GPU Operator.
• Capable of applying NVIDIA technologies to domains like fraud detection, risk management, document AI, customer service, real-time decision-making, feature engineering, and large-scale analytics.
• Ability to architect secure, observable, compliant, and cost-optimized GPU acceleration deployments across cloud, data-center, Kubernetes, and hybrid setups.

Differentiators

• Previous work experience within payments, fintech, banking technologies, financial infrastructure, fraud detection platforms, compliance technologies, or risk management systems.
• Proven ability to develop workload qualification frameworks, benchmark plans, or technical decision-making guides assisting financial services developers in determining GPU acceleration suitability.
• Experience with fraud and risk models, graph analytics, transaction intelligence, document AI, or real-time decisioning systems.
• Prior background turning NVIDIA GPU computing, AI software, model serving, or acceleration libraries into operational playbooks or production adoption plans within regulated workloads.

Additional Information

Reference Code: JR2023131