Senior Developer Relations Manager - FSI

NVIDIA Corporation; Mumbai; In loco; INR 3.000.000 - 6.000.000; Tempo pieno

Descrizione del lavoro

NVIDIA Corporation in Mumbai is hiring a senior technical DevRel leader to drive hands-on developer relations with India’s AI Labs. You will earn credibility with ML researchers and platform engineers, reading code, building demos, running benchmarks, and translating feedback into product input.

You will lead workshops, create benchmarks, and guide teams on GPU-accelerated workloads, AI model training, and real-time inference across cloud-native and edge deployments.

Competenze

• Bachelor’s degree in engineering/computer science/data science or related field.
• 8+ years of AI platforms, cloud infra, data science, or related experience.
• Strong knowledge of ML/DL, generative AI, real-time inference, and MLOps.
• Experience with regulated environments, high-availability, privacy, and security.
• Ability to profile AI workloads across compute, memory, I/O, networking, latency and cost.
• Ability to lead technical and business discussions with senior teams.
• Excellent communication and cross-functional collaboration skills.
• Working knowledge of NVIDIA technologies (NIM, Triton, TensorRT, CUDA, etc.).
• Ability to apply NVIDIA tech to fraud, risk, document AI, and real-time decisions.
• Design secure, observable GPU-accelerated deployments across cloud, data-center, Kubernetes.

Mansioni

• 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, and platform leaders.
• Identify and accelerate workloads like foundation model training, fine-tuning, speech AI, and multimodal AI.
• Profile bottlenecks and decide compute/memory/network/IO-bound issues to favor GPU acceleration.
• Build and adapt demos, sample code, notebooks, benchmarks, and reference architectures.
• Lead technical workshops, code labs, reviews, office hours, and webinars.
• Collaborate with developers to debug integration issues and optimize inference performance.
• Explain workload differences across labs due to architecture, data pipelines, batch size, and deployment.
• Capture developer feedback and product needs for NVIDIA teams.

Conoscenze

ML/AI platforms

Deep learning

Generative AI

Real-time inference

Data engineering

MLOps

Cloud-native systems

Profiling workloads

Stakeholder management

Cross-functional collaboration

Formazione

Bachelor's degree in CS/Engineering/Data Science

Strumenti

NIM

Triton

TensorRT

TensorRT-LLM

CUDA

Nsight

RAPIDS

NGC

NVIDIA AI Enterprise

GPU Operator

Descrizione del lavoro

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.