NVIDIA

1 month ago

Senior Solutions Architect - Generative AI

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Senior Solutions Architect guiding enterprise customers through end-to-end on-prem Generative AI deployments. You will design production-grade RAG systems, customize open-source foundation models, build agentic applications, optimize inference, and ensure responsible AI practices, all while mentoring on-prem AI Factory deployments and scalable Kubernetes-based orchestration.

Location: Pune

Workplace: Onsite

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Seniority: Sr. Manager level

Key Responsibilities

•Lead enterprise customers through the full lifecycle of on-prem Generative AI — from use case discovery and model selection to fine-tuning, deployment, and continuous evaluation.
•Architect production-grade RAG systems using NeMo Retriever, embedding and reranker NIMs, and vector databases such as Milvus, pgvector, and Weaviate, tuned for accuracy, latency, and cost at scale.
•Guide customers in selecting and customizing open-source foundation models like Llama, Mistral, Qwen, and Gemma, and lead them through fine-tuning workflows using NeMo Customizer, PEFT/LoRA, SFT, DPO, and RLHF.
•Compose and build agentic applications using frameworks like LangGraph, LlamaIndex, CrewAI, AutoGen, and NVIDIA AI Blueprints, covering use cases such as customer service automation, enterprise search, document intelligence, video analytics, software engineering agents, and industry-specific copilots.
•Advise on inference optimization with TensorRT-LLM, vLLM, and SGLang, including quantization, speculative decoding, and multi-LoRA serving.

Key Requirements

•5–8 years of experience as a Solutions Architect, Field Engineer, ML Platform Engineer, or similar customer-facing technical role.
•Strong hands-on background with accelerated computing infrastructure: GPUs, high-speed interconnects (InfiniBand, RoCE), parallel file systems (Lustre, GPFS, WEKA, VAST, or DDN or equivalent experience), and data center fundamentals.
•Production experience with Kubernetes and at least one workload scheduler like Run:ai, Slurm, KubeFlow, or Volcano in GPU-accelerated environments.
•Practical depth in Generative AI: LLM training/fine-tuning, RAG architectures, inference optimization (TensorRT-LLM, vLLM, SGLang), and quantization.
•Experience crafting solutions for enterprise constraints: air-gapped environments, data sovereignty, security/compliance (SOC 2, HIPAA, FedRAMP-adjacent), and multi-tenancy.

Experience:5-8 yearsAIEnterprise softwareGPU computing

Education:Bachelor's

Skills:NeMoNIMTensorRT-LLMVLLMWeaviateMilvusLlamaQwenGemmaPEFTLoRASFTRLHFLangGraphLlamaIndexCrewAIAutoGenNVIDIA AI BlueprintsKubernetesRun:ai

Languages:English

Tech Stack:DGXHGXSpectrum-XInfiniBandNVMeCUDACUDA-XDockerKubernetesNVIDIA GPU OperatorRun:aiSlurmKubeFlowTensorRT-LLMVLLMWeaviateMilvusLustreGPFSPEFT

Company Brief

NVIDIA

Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor

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NVIDIA

1 month ago

Senior Solutions Architect - Generative AI

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Location: Pune

Workplace: Onsite

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Seniority: Sr. Manager level

Key Responsibilities

•Lead enterprise customers through the full lifecycle of on-prem Generative AI — from use case discovery and model selection to fine-tuning, deployment, and continuous evaluation.
•Architect production-grade RAG systems using NeMo Retriever, embedding and reranker NIMs, and vector databases such as Milvus, pgvector, and Weaviate, tuned for accuracy, latency, and cost at scale.
•Guide customers in selecting and customizing open-source foundation models like Llama, Mistral, Qwen, and Gemma, and lead them through fine-tuning workflows using NeMo Customizer, PEFT/LoRA, SFT, DPO, and RLHF.
•Compose and build agentic applications using frameworks like LangGraph, LlamaIndex, CrewAI, AutoGen, and NVIDIA AI Blueprints, covering use cases such as customer service automation, enterprise search, document intelligence, video analytics, software engineering agents, and industry-specific copilots.
•Advise on inference optimization with TensorRT-LLM, vLLM, and SGLang, including quantization, speculative decoding, and multi-LoRA serving.

Key Requirements

•5–8 years of experience as a Solutions Architect, Field Engineer, ML Platform Engineer, or similar customer-facing technical role.
•Strong hands-on background with accelerated computing infrastructure: GPUs, high-speed interconnects (InfiniBand, RoCE), parallel file systems (Lustre, GPFS, WEKA, VAST, or DDN or equivalent experience), and data center fundamentals.
•Production experience with Kubernetes and at least one workload scheduler like Run:ai, Slurm, KubeFlow, or Volcano in GPU-accelerated environments.
•Practical depth in Generative AI: LLM training/fine-tuning, RAG architectures, inference optimization (TensorRT-LLM, vLLM, SGLang), and quantization.
•Experience crafting solutions for enterprise constraints: air-gapped environments, data sovereignty, security/compliance (SOC 2, HIPAA, FedRAMP-adjacent), and multi-tenancy.

Experience:5-8 yearsAIEnterprise softwareGPU computing

Education:Bachelor's

Skills:NeMoNIMTensorRT-LLMVLLMWeaviateMilvusLlamaQwenGemmaPEFTLoRASFTRLHFLangGraphLlamaIndexCrewAIAutoGenNVIDIA AI BlueprintsKubernetesRun:ai

Languages:English

Tech Stack:DGXHGXSpectrum-XInfiniBandNVMeCUDACUDA-XDockerKubernetesNVIDIA GPU OperatorRun:aiSlurmKubeFlowTensorRT-LLMVLLMWeaviateMilvusLustreGPFSPEFT

Company Brief

NVIDIA

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor