Backend Engineer with AI Experience

  • Richardson, TX
  • Posted 1 day ago | Updated 1 day ago

Overview

Hybrid
Depends on Experience
Accepts corp to corp applications
Contract - W2
Contract - Independent
Contract - 12 Month(s)
No Travel Required

Skills

AI
Python
Java
Golang

Job Details

* Location: Onsite role at the central lab facility in Richardson, Texas (Dallas metro area).
* Responsibilities:
o Backend engineering with a focus on Nvidia AI architectures for agentic AI, distributed retrieval augmented generation, and similar systems.
o Supporting proof-of-concept (POC) workflows related to private 5G and network initiatives.
o Comfortable interacting with customers and attending trade shows/executive briefings.
o Contributing to customer-facing activities as part of a small team.
II. Candidate Qualifications:
* Must-haves:
o Experienced backend engineer.
o Nvidia AI architecture experience.
o Hardware/IoT experience.
o Strong networking background (related to private wireless networks).
o Comfortable and conversational in front of customers.
* Preferred Skills:
o Familiarity with agentic AI systems.
o Experience with Nvidia tools and libraries.
o Cloud experience (basics of cloud deployment).
* Backend Languages: Open to various languages (Python, Java, Golang), but experience deploying Nvidia templates and building connective tissues/scripting is key.
* Experience Level: Recognizing limited experience in agentic AI (1-2 years), they value candidates who are good in front of customers and internal stakeholders.
III. Hiring Process:
* Screening Interviews
* Group Interview: 1-1.5 hours with 3-4 team members, focusing on problem-solving, collaboration, and cultural fit.
* Final Round Interview
* Timeline: Aim to complete the process within 1-2 weeks for promising candidates.
* No technical tests or homework assignments.
IV. Additional Information:
* Driving: May require occasional driving for transporting equipment and colleagues to conferences (renting vans, etc.).
* Hybrid Work: Variable, but expect 2-3 days onsite per week due to hardware involvement and lab learning. Flexibility is important.
* Focus: Leveraging existing templates and architectures provided by Nvidia.
* Compute Environment: Primarily on-premise compute managed with Proxmox and Kubernetes due to data sensitivity.

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