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Ai Reliability Engineer Jobs (NOW HIRING)

OVA.Work

AI Reliability Engineer

$58.25 - $77.50/hr

Work is seeking an AI Reliability Engineer to ensure the reliability, availability, scalability, and operational excellence of AI and machine learning systems in production. The role involves ...

iFlow Inc

SRE Engineer -AI

Redmond, WA · On-site

$63.75 - $84.75/hr

Job Title : SRE Engineer Location: Redmond,WA Duration: 6 Months Experience: 10-22 Years Description: Responsibilities: Deploy and manage AI resources on Microsoft Azure, including AI Foundry and RAG ...

Future Secure AI

Site Reliability Engineer

Austin, TX · On-site

$56.50 - $75/hr

Future Secure AI is building innovative solutions at the forefront of AI technology, seeking a Site Reliability Engineer to design, build, and operate the platforms that power AI Co-Workers. The role ...

Future Secure AI

Site Reliability Engineer

Austin, TX · On-site

$56.50 - $75/hr

Future Secure AI is building innovative solutions at the forefront of AI technology. They are seeking a Site Reliability Engineer to design, build, and operate platforms that support AI Co-Workers ...

BilgeAdam Technologies GmbH

NY · On-site

$120 - $180/hr

Qualifications * SRE practices, observability (OpenTelemetry, Datadog) * Experience with high-scale distributed systems * Knowledge of AI monitoring (drift, bias, inference metrics) #J-18808-Ljbffr

Future Secure AI

Site Reliability Engineer

Austin, TX · On-site

$56.50 - $75/hr

Future Secure AI is at the forefront of AI technology, tackling significant real-world challenges for global enterprises. They are seeking a Site Reliability Engineer to design, build, and operate ...

Etched

Reliability Engineer

Cupertino, CA

$2.0K/mo

  • Medical

  • Dental

  • Vision

About Etched Etched is building AI chips that are hard-coded for individual model architectures ... Reliability Engineer We are seeking a skilled and detail-oriented Reliability Engineer to join our ...

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How much do ai reliability engineer jobs pay per year?

As of Aug 18, 2026, the average yearly pay for ai reliability engineer in the United States is $117,973.00, according to ZipRecruiter salary data. Most workers in this role earn between $102,500.00 and $129,000.00 per year, depending on experience, location, and employer.

What is an AI reliability engineer?

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 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 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 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.

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Ai Reliability Engineer jobs near you
Infographic showing various Ai Reliability Engineer job openings in the United States as of August 2026, with employment types broken down into 76% Full Time, 21% Part Time, and 3% Contract. Highlights an 64% Physical, 4% Hybrid, and 32% Remote job distribution, with an average salary of $117,973 per year, or $56.7 per hour.

AI Reliability Engineer

OVA.Work

Remote

$58.25 - $77.50/hr

Full-time

Re-posted 13 days ago

Job description

Job Summary:
OVA.Work is seeking an AI Reliability Engineer to ensure the reliability, availability, scalability, and operational excellence of AI and machine learning systems in production. The role involves combining Site Reliability Engineering, MLOps, and cloud engineering practices to build resilient AI platforms and improve system reliability.
Responsibilities:
• Design and implement reliability engineering practices for AI and machine learning platforms.
• Monitor the availability, latency, throughput, and health of AI services and model inference endpoints.
• Develop Service Level Indicators (SLIs), Service Level Objectives (SLOs), and Service Level Agreements (SLAs) for AI systems.
• Build automated monitoring, alerting, incident response, and self-healing capabilities.
• Improve the reliability, scalability, and resilience of AI infrastructure and model-serving platforms.
• Collaborate with AI Engineers, Data Scientists, Platform Engineers, DevOps Engineers, and Software Engineers to enhance production stability.
• Automate operational tasks using scripting and Infrastructure as Code (IaC).
• Support deployment, rollback, and release strategies for AI services.
• Investigate production incidents, conduct root cause analysis (RCA), and implement preventive measures.
• Monitor model performance, data quality, model drift, and inference reliability.
• Optimize cloud infrastructure, GPU utilization, and resource efficiency.
• Implement disaster recovery, backup, failover, and business continuity strategies.
• Ensure compliance with security, governance, and operational best practices.
• Develop operational dashboards, runbooks, and reliability metrics.
Qualifications:
Required:
• Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related field.
• 4+ years of experience in Site Reliability Engineering (SRE), DevOps, Platform Engineering, Cloud Engineering, or MLOps.
• Experience supporting AI or machine learning applications in production.
• Strong programming skills in Python, Go, or Bash.
• Hands-on experience with Linux administration.
• Experience with Docker and Kubernetes.
• Experience with AWS, Microsoft Azure, or Google Cloud Platform.
• Experience with CI/CD tools such as GitHub Actions, GitLab CI, Azure DevOps, or Jenkins.
• Experience with Infrastructure as Code tools such as Terraform or Pulumi.
• Strong understanding of distributed systems, networking, and cloud architecture.
• Experience with monitoring and observability platforms such as Prometheus, Grafana, OpenTelemetry, ELK Stack, Datadog, or Splunk.
Preferred:
• Experience with MLOps platforms such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure Machine Learning.
• Experience supporting Large Language Models (LLMs) and Generative AI applications.
• Knowledge of model serving technologies such as KServe, NVIDIA Triton Inference Server, Ray Serve, or BentoML.
• Experience implementing AI model monitoring, drift detection, and performance analytics.
• Familiarity with vector databases and Retrieval-Augmented Generation (RAG) architectures.
• Experience with GPU infrastructure and inference optimization.
• Knowledge of chaos engineering and resilience testing.
• Understanding of Responsible AI, governance, and operational compliance.
Company:
OVA is the most advanced Automated, Intelligent, intuitive On-boarding platform for Staffing Firms of all sizes. Founded in 2018, the company is headquartered in Alpharetta, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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