Nvidia AI Architect Delivery

Job description

Nvidia AI Architect- Delivery

**General Information**

Req #

WD00082748

Career area:

Artificial Intelligence

Country/Region:

India

State:

Karnataka

City:

BANGALORE

Date:

Monday, May 19, 2025

Working time:

Full-time

**Additional Locations** :
* India - Karnātaka - Bangalore

* India - Karnātaka - BANGALORE

**Why Work at Lenovo**

We are Lenovo.

We do what we say.

We own what we do.

We WOW our customers.

Lenovo is a US$69 billion revenue global technology powerhouse, ranked #196 in the Fortune Global 500, and serving millions of customers every day in 180 markets.

Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world’s largest PC company with a full-stack portfolio of AI-enabled, AI-ready, and AI-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services.

Lenovo’s continued investment in world-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere.

Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY).

This transformation together with Lenovo’s world-changing innovation is building a more inclusive, trustworthy, and smarter future for everyone, everywhere.

To find out more visit www.lenovo.com , and read about the latest news via ourStoryHub (https://news.lenovo.com/) .

**Description and Requirements**

**Key Responsibilities** :

+ Lead end-to-end transitions of AI PoCs into production environments, managing the entire process from testing to final deployment.
+ Configure, install, and validate AI systems using key platforms, including VMware ESXi and vSphere for server virtualization, Linux (Ubuntu/RHEL) and Windows Server for operating system integration,
+ Docker and Kubernetes for containerization and orchestration of AI workloads.
+ Conduct comprehensive performance benchmarking and AI inferencing tests to validate system performance in production.
+ Optimize deployed AI models for accuracy, performance, and scalability to ensure they meet production-level requirements and customer expectations.
+ Serve as the primary technical lead/SME for the AI POC deployment in enterprise environments, focusing on AI solutions powered by Nvidia GPUs.
+ Work hands-on with Nvidia AI Enterprise and GPU-accelerated workloads, ensuring efficient deployment and model performance using frameworks such as PyTorch and TensorFlow.
+ Lead technical optimizations aimed at resource efficiency, ensuring that models are deployed effectively within the customer’s infrastructure.
+ Ensure the readiness of customer environments to handle, maintain, and scale AI solutions post-deployment.

+ take ownership of AI project deployments, overseeing all phases from planning to final deployment, ensuring that timelines and deliverables are met.
+ Collaborate with stakeholders, including cross-functional teams (e.g., Lenovo AI Application, solution architects), customers, and internal resources to coordinate deployments and deliver results on schedule.
+ Implement risk management strategies and develop contingency plans to mitigate potential issues such as hardware failures, network bottlenecks, and software incompatibilities.
+ Maintain ongoing, transparent communication with all relevant stakeholders, providing updates on project status and addressing any issues or changes in scope.

**Experience** :

+ Overall experience 7-10 years
+ Relevant experience of 2-4 years in deploying AI/ML models/ AI solutions using Nvidia GPUs in enterprise production environments.
+ Demonstrated success in leading and managing complex AI infrastructure projects, including PoC transitions to production at scale.

**Technical Expertise:**

+ Experience in the area of Retrieval Augmented Generation (RAG), NVIDIA AI Enterprise, NVIDIA Inference Microservices (NIMs), Model Management, Kubernetes
+ Extensive experience with Nvidia AI Enterprise, GPU-accelerated workloads, and AI/ML frameworks such as PyTorch and TensorFlow.
+ Proficient in deploying AI solutions across enterprise platforms, including VMware ESXi, Docker, Kubernetes, and Linux (Ubuntu/RHEL) and Windows Server environments.
+ MLOps proficiency with hands-on experience using tools such as Kubeflow, MLflow, or AWS SageMaker for managing the AI model lifecycle in production.
+ Strong understanding of virtualization and containerization technologies to ensure robust and scalable deployments.

**Additional Locations** :
* India - Karnātaka - Bangalore

* India - Karnātaka - BANGALORE

* India

* India - Karnātaka

* India - Karnātaka - Bangalore , * India - Karnātaka - BANGALORE

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Required Skill Profession

Other General