---
title: "Hire NVIDIA AI Agent Developers | AI-Q Blueprint, NeMo Agent Toolkit & Deep Agents | Vindler"
description: "Hire senior engineers to build enterprise AI agents with NVIDIA AI-Q Blueprint, NeMo Agent Toolkit, Nemotron models, and OpenShell security. From deep research agents to production multi-agent systems on GPU infrastructure."
url: "https://vindler.solutions/hire/nvidia-ai-agents"
locale: "en"
updatedAt: "2026-11-12"
---

# Enterprise AI Agents with NVIDIA Agent Toolkit

Build production AI agents with NVIDIA AI-Q Blueprint, NeMo Agent Toolkit, and OpenShell security. Deep research agents, multi-agent orchestration, and GPU-accelerated compute deployed on your infrastructure or ours.

Tell Us About Your Project

Technology Partners

[AWS Partner Network](https://aws.amazon.com/partners/)[LangChain](https://www.langchain.com/)

Recognized by Clutch

[Top Clutch AI Consulting Company Brazil 2026](https://clutch.co/profile/vindler-solutions)[Top Clutch AI Company Brazil 2026](https://clutch.co/profile/vindler-solutions)[Top Clutch AI Company São Paulo 2026](https://clutch.co/profile/vindler-solutions)[Top Clutch Chatbot Company Brazil 2026](https://clutch.co/profile/vindler-solutions)

## What We Build with NVIDIA Agent Toolkit

From AI-Q deep research agents to OpenShell-secured multi-agent systems, we deliver enterprise-grade AI agents that run on GPU infrastructure.

### AI-Q Deep Research Agents

Deploy NVIDIA's AI-Q Blueprint for enterprise deep research systems that rank #1 on DeepResearch benchmarks. We customize the LangGraph-based state machine to connect to your enterprise data sources, configure hybrid model routing with Nemotron, and build evaluation harnesses that measure accuracy and cost per query.

### Multi-Agent Orchestration with NeMo

Build production multi-agent systems using NeMo Agent Toolkit for profiling, optimization, and evaluation. We design agent teams with strict context isolation, sub-agent spawning, and long-term memory so your agents can run complex workflows for hours across dozens of steps without token bloat.

### OpenShell-Secured Agent Deployments

Enterprise agents need enterprise security. We deploy NVIDIA OpenShell to sandbox autonomous agents with kernel-level isolation using Landlock, Seccomp, and OPA/Rego policies. Declarative YAML guardrails control filesystem access, network activity, privilege escalation, and model API routing.

### GPU-Accelerated Agent Compute

Agents that process large datasets need GPU acceleration. We build deep agent workflows with CUDA-X compute sandboxes, using cuDF for structured data manipulation and NeMo Curator for petabyte-scale data curation. Ideal for financial services, healthcare, and data-intensive enterprise applications.

### Nemotron Model Deployment

Deploy NVIDIA Nemotron models (Nano, Super, Ultra) via NIM microservices for on-prem or cloud inference. We configure hybrid architectures that use frontier models for orchestration and Nemotron open models for research tasks, cutting query costs by more than 50% while maintaining world-class accuracy.

### Enterprise Knowledge Agents

Build AI agents that perceive, reason, and act on your enterprise knowledge. We connect AI-Q to your internal data sources, configure automatic source selection and depth-of-analysis controls, and implement citation tracking with built-in evaluation systems that explain how each answer is produced.

Built by Senior Engineers

## Why NVIDIA Agents Need Senior Engineers

NVIDIA announced the Agent Toolkit at GTC 2026 and seventeen major enterprises adopted it in the same week: Adobe, Salesforce, SAP, Cisco, CrowdStrike, ServiceNow, and more. The tools are open source and powerful. The challenge is not accessing them. The challenge is deploying them correctly in a production environment where security, cost, and reliability actually matter.

AI-Q Blueprint gives you a reference deep research agent. But a reference implementation is not a production system. Production means configuring OpenShell security policies that match your compliance requirements without breaking agent functionality. It means profiling agent teams with NeMo Agent Toolkit to find the bottlenecks that only appear at scale. It means designing hybrid model routing that balances Nemotron cost savings against frontier model accuracy for your specific data and queries.

We have deployed multi-agent systems across automotive, healthcare, financial services, and enterprise SaaS. We know LangGraph state machines inside out because we have been building with LangChain and LangGraph since before the NVIDIA partnership was announced. When you hire our team, you get engineers who understand both the NVIDIA stack and the agent framework beneath it.

## Our Tech Stack

The full NVIDIA Agent Toolkit stack integrated with the LangChain ecosystem and enterprise cloud infrastructure.

NVIDIA AI-Q Blueprint

NeMo Agent Toolkit

NVIDIA OpenShell

Nemotron (Nano/Super/Ultra)

NVIDIA NIM

NemoClaw

LangChain

LangGraph

LangSmith

CUDA-X / cuDF

Python

Docker

Kubernetes / Helm

AWS EKS

DGX / RTX

LangFuse

FastAPI

## AI Agent Projects We Have Delivered

Real results from production AI agent deployments.

[90% automation rate

### Multi-Agent Systems Architecture

Designed a multi-agent system where specialized AI agents handle research, analysis, and reporting in coordinated workflows, replacing manual processes that took days.

Read Case Study](https://vindler.solutions/case-studies/multi-agent-systems-architecture)[35% increase in customer satisfaction

### AI Sales Assistant with RAG

Built an AI-powered sales assistant that retrieves real-time product information, handles objections with sourced answers, and integrates directly into the existing CRM workflow.

Read Case Study](https://vindler.solutions/case-studies/ai-sales-assistant-rag)[MVP deployed in 2 weeks

### Voice AI for Medicare Patients

Shipped a production voice AI assistant for Medicare patients with sub-500ms response times, multimodal interface, and full pipeline observability.

Read Case Study](https://vindler.solutions/case-studies/medicare-voice-assistant)

## How We Work

From architecture assessment to production deployment with NVIDIA Agent Toolkit.

Step 1

### Architecture Assessment

We start with a deep technical conversation about your data landscape, security requirements, and agent objectives. We assess whether AI-Q, custom NeMo agents, or a hybrid approach fits your use case and infrastructure.

Step 2

### Blueprint & Security Design

We deliver a detailed architecture proposal covering agent topology, model selection (Nemotron vs frontier), OpenShell security policies, deployment strategy (cloud, on-prem, or hybrid), and cost projections with hybrid model routing optimization.

Step 3

### Deploy & Optimize

We deploy iteratively with NeMo Agent Toolkit profiling at every stage. You get working agents from week one, with LangSmith tracing, evaluation benchmarks, and cost-per-query dashboards. We optimize agent performance continuously until your system meets production SLAs.

## Frequently Asked Questions

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## Ready to Deploy Enterprise AI Agents?

Tell us about your agent use case and we will respond within 24 hours with an architecture assessment. No commitment, no pressure, just a technical conversation about the right NVIDIA stack for your requirements.

Free architecture assessment call

Blueprint and security design within one week

Working agents from week one of engagement

### Get a Free Assessment

Describe your AI agent project and we'll send you an architecture assessment within 24 hours.

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