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Assistant Gpu Jobs in Decatur, GA (NOW HIRING)

DigitalOcean

Senior Data Center Engineer

Atlanta, GA · On-site

$105K - $132K/yr

Experience deploying and troubleshooting GPU Hardware * Strong understanding of operating systems ... Can assist with budgetary and planning requirements for future projects and initiatives * Ability ...

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Assistant Gpu information

What is the difference between Assistant Gpu vs Assistant Data Analyst?

AspectAssistant GpuAssistant Data Analyst
Required CredentialsAssociate degree or relevant certifications in GPU technology or IT supportAssociate degree or certifications in data analysis, statistics, or related fields
Work EnvironmentIT departments, hardware labs, tech companiesBusiness, finance, or tech companies focusing on data insights
Employer & Industry UsageTechnology firms, hardware manufacturers, gaming companiesFinancial institutions, marketing agencies, tech firms
Common Search & ComparisonYesYes

The Assistant Gpu and Assistant Data Analyst roles share similarities in required certifications and work environments, but they focus on different technical areas. The Assistant Gpu specializes in hardware and GPU technology support, while the Assistant Data Analyst concentrates on data interpretation and analysis. Both roles are essential in their respective industries and often searched together by professionals seeking technical support or entry-level positions in tech-related fields.

What cities near Decatur, GA are hiring for Assistant Gpu jobs?

Cities near Decatur, GA with the most Assistant Gpu job openings:

Assistant Gpu jobs near you

AI Reliability Engineer (AI SRE) - Q126

R2 Technologies Corporation

Alpharetta, GA • On-site

$55.75 - $74/hr

Full-time

Re-posted 16 days ago

Job description

Overview:
Job Title: AI Reliability Engineer (AI SRE)
Company: R2 Technologies
Location: Alpharetta, GA (Hybrid / Remote Options Available)
Employment Type: Full-Time / Contractual
About R2 Technologies: R2 Technologies is a Certified Minority Business Enterprise (MBE) headquartered in Alpharetta, GA. With over two decades of experience across global markets, we have built a reputation as a trusted partner for IT staffing excellence and cutting-edge digital product innovation. We are driven by innovation and operate on a simple philosophy: "We deliver what we promise, and we promise only what we can deliver." Beyond providing top-tier IT talent, R2 builds cutting-edge proprietary solutions like SmartEnt-an Enterprise AI & IoT Intelligence Platform utilizing advanced NLP and AI technologies. By partnering closely with our clients, we deliver technology-driven outcomes that are realistic, measurable, and impactful.
Job Summary: As enterprise AI shifts from prototypes to mission-critical production systems, we need engineers who can guarantee stability. R2 Technologies is seeking an AI Reliability Engineer to merge traditional Site Reliability Engineering (SRE) with LLM operations. You will be the guardian of our production AI, responsible for monitoring foundation models for performance drift, optimizing token usage and GPU costs, and ensuring high-availability inference for our SmartEnt platform.
Key Responsibilities: * Deploy, scale, and manage LLM inference servers (e.g., vLLM, Ray Serve, NVIDIA Triton) on Kubernetes across multi-cloud environments.
  • Implement comprehensive observability, logging, and tracing for complex agentic workflows using platforms like LangSmith, MLflow, or Weights & Biases (Weave).
  • Monitor production models for data drift, hallucination rates, and latency spikes, implementing automated rollback or model-routing strategies when necessary.
  • Optimize cloud infrastructure to balance GPU utilization, inference speed, and token cost (FinOps for AI).
  • Automate infrastructure provisioning (IaC) and CI/CD pipelines specifically tailored for machine learning models and fine-tuned adapters.
  • Actively utilize AI-assisted coding tools (GitHub Copilot, Cursor) to automate infrastructure management and incident response scripting.

Qualifications: * Up to 3 years of hands-on experience in SRE, DevOps, MLOps, or Cloud Infrastructure.
  • Strong proficiency in containerization and orchestration (Docker, Kubernetes, Helm).
  • Experience configuring and scaling GPU-backed workloads in cloud environments (AWS, Azure, or GCP).
  • Familiarity with LLM observability tools and trace-level debugging of AI applications.
  • Proven experience or strong familiarity working alongside AI coding assistants to enhance productivity.
  • Scripting skills in Python and Bash, with a strong focus on system reliability, automation, and cost-optimization.

Skills:
Reliability Engineering,Kubernetes

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