Senior Solutions Architect, Agentic AI

Nvidia

Confirmed live yesterday High trust

Remote

Quick summary

Work type: Remote
Location: Remote
Salary: $184,000–$287,500 / yr
Posted: 57 days ago
Freshness: Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $208k

This role $236k

$151k most similar roles pay here $302k

This role pays more than 72% of similar roles. Most pay $169,500–$246,150 — the shaded band above. At the midpoint, this role pays about $236k versus about $208k for comparable roles.

Based on 239 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

Most-posted roles

View all roles at Nvidia

At a glance

TL;DR · Senior Solutions Architect, Agentic AI

Role Posting Log in to save

Senior Solutions Architect, Agentic AI joins the team to help Enterprise ISVs design, build, and deploy production-grade Agentic AI systems on NVIDIA’s accelerated computing platform. This role involves leading technical delivery from discovery through architecture, prototyping, and performance optimization for multi-agent workflows, RAG-integrated applications, tool use, and model customization. You will conduct deep architecture reviews regarding latency, cost, and reliability while building hands-on PoCs and reference architectures. The position requires expertise in Python, Linux, PyTorch, or TensorFlow, alongside experience with frameworks like LangGraph, LlamaIndex, LangChain, CrewAI, and Semantic Kernel. Candidates must possess skills in post-training workflows such as SFT, RLHF, DPO, and PEFT/LoRA. You will solve complex technical challenges by translating frontier AI capabilities into reliable enterprise products while providing feedback to improve internal platforms and tools.

What does a Solutions Architect earn in Remote?

Median $231100 from 71 postings across 13 companies.

See salary data

Skills

Agentic AI RAG Python PyTorch TensorFlow NeMo Framework LangChain LlamaIndex LangGraph CrewAI Semantic Kernel SFT RLHF DPO PEFT LoRA TensorRT-LLM Triton Linux

What you'll do

Design and build enterprise-grade agentic systems including multi-agent workflows, RAG integration, and tool-use patterns.
Lead technical delivery for partner engagements from initial discovery and architecture through production rollout.
Conduct deep architecture reviews to balance model quality, latency, cost, and safety requirements.
Develop hands-on prototypes, benchmarks, and reusable reference architectures for enterprise partners.
Guide partners through model customization workflows including fine-tuning, RLHF/DPO, and synthetic data generation.
Implement agentic systems using frameworks such as LangGraph, LlamaIndex, LangChain, or CrewAI.
Translate partner deployment feedback into actionable product improvements for NVIDIA engineering teams.

What we're looking for

BS/MS/PhD in Computer Science, Electrical Engineering, AI/ML, or equivalent experience.
8+ years of engineering, solutions architecture, applied ML, or technical deployment experience.
Proven track record leading complex AI, ML, distributed systems, or enterprise software deployments from prototype to production.
Hands-on experience building LLM, generative AI, RAG, or agentic AI applications in production environments.
Strong programming and debugging skills in Python and Linux using PyTorch or TensorFlow.
Deep understanding of agentic AI architectures including tool use, orchestration, memory, retrieval, planning, evaluation, and guardrails.
Experience with model customization techniques such as SFT, RL/RLHF/RLAIF, DPO, LoRA/PEFT, and quantization-aware optimization.
Ability to lead ambiguous engagements and communicate effectively with both technical and executive audiences.