NVIDIA’s Solutions Architect team is looking for a senior, highly hands-on Solutions Architect. The role involves developing, building, and deploying agentic AI systems with top Media & Entertainment (M&E) companies. Partnering with customers from studios, streaming and broadcast platforms, gaming, advertising, content creation, and the media supply chain, you will transform frontier large language model (LLM) capabilities into autonomous agents at production scale. These agents will redefine how content is created, personalized, distributed, and monetized.
This is a builder’s role! You will spend time architecting and writing code. You will develop multi-agent systems, retrieval pipelines, and optimized inference stacks on NVIDIA’s full-stack accelerated computing platform. We want a creative, diligent, and curious engineer energized by agentic AI and ready to make significant change. If that’s you, join us!
What you’ll be doing:
Architect, build, and ship end-to-end Agentic AI applications for M&E use cases—spanning multi-agent coordination, long-horizon reasoning, planning, and tool use—along with high-performance RAG pipelines over heterogeneous media assets (text, code, images, audio, video) to tackle real production challenges such as content generation, localization, metadata enrichment, personalization, recommendation, and ad operations.
Act as a hands-on technical advisor and main domain expert during the pre- and post-sale stages. Work closely with customer AI researchers, engineers, and developers to build, prototype, and deploy Agentic solutions on NVIDIA platforms.
Optimize inference performance and total cost of ownership using the full NVIDIA AI inference stack—and build hands-on proofs-of-concept, reference architectures, and reusable blueprints that serve as production templates and accelerate time-to-value. Post-training open sourced models to meet the M&E requirements.
Partner with NVIDIA engineering, product, and sales teams to secure build wins, translate customer feedback into actionable product and roadmap insights, and scale global expertise through technical collateral, workshops, and developer communities.
What we need to see:
BS/MS/PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, AI/ML, or a related field (or equivalent experience)
8+ years as an ML/Software Engineer or Solutions Architect writing production-level code in Python and/or C/C++ in Linux environments.
Validated experience building sophisticated agentic and multi-agent AI systems using orchestration frameworks such as LangGraph, LlamaIndex, CrewAI, LangChain, OpenAI Agents SDK —including tool-using and routing agents. Solid understanding of MCP and A2A is vital.
Strong background in PyTorch and distributed GPU (post-)training. Able to quickly prototype and build scalable GPU-accelerated architectures. Applies test-time compute, reinforcement learning, inference optimization, and post-training. Deploys workloads at scale on public cloud (AWS, GCP, Azure, OCI) or on-premise.
Strong grasp of the M&E industry paired with excellent communication and presentation skills. Able to explain sophisticated ideas to both technical and non-technical groups. Works well with executives, partners, and engineering teams. Leads projects from start to finish in a fast-paced, multitasking setting.
Ways to stand out from the crowd:
Practical experience working directly with the NVIDIA agentic AI software stack—NVIDIA NIM, NeMo Framework, NeMo Retriever, NeMo Agent Toolkit, Dynamo, Triton Inference Server, TensorRT-LLM, and AI Blueprints.
Expertise building LLM evaluation harnesses, benchmarking systems, observability platforms, and safety guardrails, plus fine-tuning and optimizing reasoning-focused LLMs and SLMs through timely engineering and quantization.
Experience developing production-grade deployment patterns using Kubernetes/OpenShift, CI/CD automation, and secure cloud-native infrastructure, with familiarity with modern agent architectures and emerging communication protocols such as MCP (Model Context Protocol) or Google A2A.
Proven experience handling NVIDIA GPU architectures, CUDA-X libraries (cuBLAS, cuDNN, RAPIDS), and HPC technologies (NCCL, InfiniBand, MPI, NVLink), along with familiarity in large-scale data processing and distributed/parallel computing frameworks (e.g., Spark, Dask).
A strong public profile (blogs, GitHub, conference talks) that demonstrates your expertise and passion for agentic AI.
Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- BS/MS/PhD in Computer Science, EE, Physics, Mathematics, AI/ML, or related field (or equivalent experience)
- 8+ years as an ML/Software Engineer or Solutions Architect
- Production-level coding experience in Python and/or C/C++ in Linux environments
- Validated experience building agentic and multi-agent AI systems using orchestration frameworks (e.g., LangGraph, LlamaIndex, CrewAI, LangChain, OpenAI Agents SDK)
- Solid understanding of MCP and A2A (Model Context Protocol / Agent-to-Agent communication)
- Strong background in PyTorch and distributed GPU (post-)training, including prototyping scalable GPU-accelerated architectures
- Experience deploying AI workloads at scale on public cloud (AWS, GCP, Azure, OCI) or on-premise
- Strong grasp of the Media & Entertainment industry and excellent communication/presentation skills for technical and non-technical audiences
- Practical experience with NVIDIA agentic AI stack (NVIDIA NIM, NeMo Framework, NeMo Retriever, NeMo Agent Toolkit, Dynamo, Triton, TensorRT-LLM, AI Blueprints)
- Expertise in LLM evaluation, benchmarking, observability, safety guardrails, fine-tuning, and quantization
- Experience with Kubernetes/OpenShift, CI/CD automation, and secure cloud-native infrastructure
- Experience with NVIDIA GPU architectures and CUDA-X libraries (cuBLAS, cuDNN, RAPIDS), NCCL, InfiniBand, MPI, NVLink, and large-scale data processing frameworks (Spark, Dask)
- Public technical profile (blogs, GitHub, conference talks) demonstrating expertise in agentic AI
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”






