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Ai Reliability Engineer Jobs in Atlanta, GA (NOW HIRING)

anduril

Senior Reliability Engineer

Atlanta, GA

Senior Reliability Engineer Atlanta, Georgia, United States Anduril Industries is a defense ... Anduril's family of systems is powered by Lattice OS, an AI-powered operating system that turns ...

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Patientco

Site Reliability Engineer II

Atlanta, GA

$54.75 - $72.75/hr

As an SRE Specialist, you'll work closely with engineering, product, and data teams to ensure our ... Active use of artificial intelligence (AI) tools and techniques to enhance performance, drive ...

Crew Career Center

Senior SRE

Atlanta, GA

$54.75 - $72.75/hr

As an SRE Specialist, you'll work closely with engineering, product, and data teams to ensure our ... Active use of artificial intelligence (AI) tools and techniques to enhance performance, drive ...

Crew Career Center

Site Reliability Engineer II

Atlanta, GA

$54.75 - $72.75/hr

As an SRE Specialist, you'll work closely with engineering, product, and data teams to ensure our ... Active use of artificial intelligence (AI) tools and techniques to enhance performance, drive ...

Patientco

Senior SRE

Atlanta, GA · On-site

$54.75 - $72.75/hr

As an SRE Specialist, you'll work closely with engineering, product, and data teams to ensure our ... Active use of artificial intelligence (AI) tools and techniques to enhance performance, drive ...

Waystar

Senior SRE

Atlanta, GA · On-site

$54.75 - $72.75/hr

As an SRE Specialist, you'll work closely with engineering, product, and data teams to ensure our ... Active use of artificial intelligence (AI) tools and techniques to enhance performance, drive ...

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Ai Reliability Engineer information

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$58.7K

$113.4K

$135.6K

How much do ai reliability engineer jobs pay per year?

As of May 28, 2026, the average yearly pay for ai reliability engineer in Atlanta, GA is $113,449.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,600.00 and $124,100.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an AI Reliability Engineer, and why are they important?

To thrive as an AI Reliability Engineer, you need a solid background in computer science or engineering, expertise in AI/ML concepts, and experience with software testing and reliability methodologies. Familiarity with tools like TensorFlow, PyTorch, CI/CD pipelines, and reliability testing frameworks, along with certifications in cloud platforms (e.g., AWS Certified Machine Learning), is highly valuable. Analytical thinking, problem-solving abilities, and strong collaboration skills set top performers apart in this role. These skills ensure robust, dependable AI systems that meet performance standards and maintain trust in critical applications.

What are some common challenges Ai Reliability Engineers face when ensuring model robustness in production environments?

Ai Reliability Engineers often encounter challenges such as monitoring AI model performance for drift or unexpected behavior, managing data quality issues, and implementing automated alerting systems for anomalies. In production, it's crucial to ensure that AI models operate consistently and remain reliable under varying conditions and data inputs. Collaborating closely with data scientists, software engineers, and DevOps teams is essential to address these challenges and to continuously improve model reliability and uptime.

What are AI Reliability Engineers?

AI Reliability Engineers are professionals responsible for ensuring that artificial intelligence systems function reliably, safely, and effectively over time. They work on monitoring AI models in production, identifying and mitigating potential failures, and improving the robustness of AI systems. Their tasks often include testing, validation, performance monitoring, and implementing best practices for maintaining AI infrastructure. By focusing on reliability, they help organizations deploy AI solutions that are dependable and trustworthy in real-world environments.

What is a $900,000 AI job?

A $900,000 AI job typically refers to highly senior roles such as AI executives, chief AI officers, or lead AI engineers at top technology companies, often involving advanced expertise in machine learning, deep learning, and AI strategy. These positions usually require extensive experience, specialized skills, and may include performance-based bonuses or stock options that contribute to the high total compensation.

What is the difference between Ai Reliability Engineer vs Data Scientist?

AspectAi Reliability EngineerData Scientist
Required CredentialsBachelor's or master's in CS, engineering, or related; certifications in AI/MLBachelor's or master's in CS, statistics, or related; certifications in data analysis or ML
Work EnvironmentTech companies, AI-focused teams, engineering departmentsResearch labs, tech firms, analytics teams
Employer & Industry UsageAI product development, machine learning systems, reliability testingData analysis, predictive modeling, business insights

While both roles involve AI and ML, Ai Reliability Engineers focus on ensuring AI system robustness and uptime, whereas Data Scientists analyze data to generate insights and models. The roles often collaborate but serve different primary functions within AI projects.

What are popular job titles related to Ai Reliability Engineer jobs in Atlanta, GA? For Ai Reliability Engineer jobs in Atlanta, GA, the most frequently searched job titles are:
What job categories do people searching Ai Reliability Engineer jobs in Atlanta, GA look for? The top searched job categories for Ai Reliability Engineer jobs in Atlanta, GA are:
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Ai Reliability Engineer jobs near you
AI Reliability Engineer (AI SRE) - Q126

AI Reliability Engineer (AI SRE) - Q126

R2 Technologies Corporation

Alpharetta, GA • On-site

$55.75 - $74/hr

Full-time

Posted 20 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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