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Contractual Observability Engineer Jobs in Georgia

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Contractual Observability Engineer information

What does a contractual observability engineer do?

A contractual observability engineer designs and implements systems to monitor and analyze the performance, reliability, and security of software applications and infrastructure. They work with tools like monitoring dashboards, logging, and alerting systems to ensure system health and troubleshoot issues efficiently, often requiring knowledge of cloud environments and scripting skills.

What is the difference between Contractual Observability Engineer vs Site Reliability Engineer?

AspectContractual Observability EngineerSite Reliability Engineer
Primary FocusImplementing observability tools, monitoring, and logging systemsEnsuring system reliability, scalability, and performance
Skills & CertificationsMonitoring tools, scripting, cloud platformsSystems engineering, automation, incident management
Work EnvironmentDevOps teams, cloud environments, monitoring platformsOperations teams, production systems, automation tools
Industry UsageTech companies, SaaS providers, cloud servicesLarge-scale tech firms, internet services, cloud providers

While both roles involve technical expertise in cloud and systems, a Contractual Observability Engineer primarily focuses on implementing and maintaining observability tools, whereas a Site Reliability Engineer emphasizes system reliability and performance. Understanding these differences helps organizations assign the right responsibilities and skills to each role.

What is the average salary of a contractual observability engineer?

The average salary of a contractual observability engineer typically ranges from $80,000 to $130,000 annually, depending on experience, location, and specific skills such as monitoring tools and cloud platforms. Contract roles may pay hourly rates between $40 and $80, with variations based on project scope and duration.
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Contractual Observability Engineer jobs near you

AI Reliability Engineer (AI SRE) - Q126

R2 Technologies Corporation

Alpharetta, GA • On-site

$55.75 - $74/hr

Full-time

Re-posted 21 hours 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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